{ "counts": { "account_links": 24, "api_routes": 69, "cli_commands": 64, "customization_dimensions": 8, "journeys": 12, "readiness_groups": 8, "readiness_requirements": 57, "route_groups": 214, "route_surfaces": 7, "routes": 929, "tui_views": 32 }, "definition": { "category": "Kolm is the verifiable AI compiler and artifact contract layer: traces and specs become portable signed artifacts with eval gates, receipts, runtime targets, and governance.", "spine": "evidence -> dataset -> eval -> build decision -> artifact -> runtime -> receipt -> governance export" }, "dimensions": [ { "id": "model-provider", "label": "Model provider", "options": [ "OpenAI", "Anthropic Claude", "OpenRouter", "Gemini/OpenAI-compatible", "local .kolm", "self-hosted OpenAI-compatible" ], "required_affordance": "Every inference and teacher path must let the user choose or override the provider/model." }, { "id": "compute-target", "label": "Compute target", "options": [ "local CPU", "local CUDA", "Apple 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verification", "artifact diff", "eval replay", "benchmark JSON", "compliance binder" ], "required_affordance": "Every shipped model claim must have a verifier, replay, or benchmark path the user can run themselves." } ], "journeys": [ { "account": [ "/account/overview", "/account/api-control-center" ], "api": [ "POST /v1/chat/completions", "POST /v1/capture/openai", "POST /v1/capture/anthropic", "POST /v1/capture/openrouter", "POST /v1/capture/log", "GET /v1/capture/stream", "GET /v1/capture/health", "GET /v1/capture/rbac/policy" ], "cli": [ "kolm capture --provider openai --as local", "kolm capture --provider anthropic --as local", "kolm capture status", "kolm tail captures" ], "customization_dimensions": [ "model-provider", "privacy-mode", "deployment-mode" ], "evidence_paths": [ "src/product-experience.js", "src/router.js", "src/daemon-connector.js", "public/account/overview.html", "public/account/api-control-center.html" ], "happy_path": [ "connect provider", "send request", 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"Filter the lake by provider, model, status, namespace, and latency.", "priority": "P0", "value": "/account/overview" }, { "kind": "command", "label": "Capture sample calls or import a JSONL corpus.", "priority": "P1", "value": "kolm lake stats --json" } ], "proof_commands": [ "kolm lake stats --json", "kolm privacy report --json", "kolm privacy scan --json" ], "stage": "observe", "status_fields": [ "events_24h", "redaction_counts", "dp_epsilon", "storage_backend" ], "surface": "privacy-lake", "tui": [ "privacy-events", "storage-sync" ], "user_story": "As a regulated operator, I can inspect what was captured, redact sensitive values, export rows, and switch to zero-retention or differential-privacy aggregates.", "ux_contract": [ "filter chips for common slices", "DP toggle for aggregates", "export button", "storage truth panel" ] }, { "account": [ "/account/train", "/account/overview" ], "api": [ "GET /v1/datasets", "POST /v1/datasets", "POST /v1/datasets/:id/split", "GET 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"evidence_paths": [ "src/build-strategy-brain.js", "scripts/build-strategy-brain.mjs", "src/distill-pipeline.js", "src/distill-strategy.js", "src/benchmark-evidence.js", "scripts/benchmark-evidence.mjs", "src/remote-compute.js", "src/compute/registry.json", "public/account/train.html" ], "happy_path": [ "choose dataset", "choose teacher", "choose student", "choose compute", "run eval-gated build" ], "id": "train-distill", "name": "Train And Distill", "next_actions": [ { "href": "/account/train", "kind": "account", "label": "Choose teacher, student, compute backend, eval gate, and output target before starting a build.", "priority": "P0", "value": "/account/train" }, { "kind": "command", "label": "Create a dataset, run `kolm distill strategy --json`, or run `kolm bench evidence --summary` before making benchmark claims.", "priority": "P1", "value": "kolm train plan --strategy" } ], "proof_commands": [ "kolm build plan --task generation --rows 1500 --holdout-pairs 300 --no-local-gpu --json", "kolm cloud readiness --json", "kolm cloud broker --json", "kolm train plan --strategy", "kolm distill strategy --json", "kolm bench evidence --summary", "kolm distill runs --json" ], "stage": "train", "status_fields": [ "build_state", "teacher_model", "student_model", "compute_target", "k_score_gate" ], "surface": "train-distill", "tui": [ "builds", "multimodal-bakeoff", "compile", "pipeline-orchestrator", "ab-experiments", "token-dpo", "reasoning-distill" ], "user_story": "As a user without perfect local hardware, I can train or distill via local compute, remote SSH GPU, managed GPU, Together, OpenAI/Claude teachers, or BYOC.", "ux_contract": [ "failure diagnostics", "cost quote before cloud run", "holdout gate visible", "provider/model selector" ] }, { "account": [ "/account/train", "/account/overview" ], "api": [ "GET /v1/models", "GET /v1/models/manifest", "GET /v1/models/cache", "GET /v1/models/recommend", "GET /v1/models/info/:id", "GET /v1/devices/recommend" ], "cli": [ "kolm models list --json", "kolm models recommend --json", "kolm models devices --json", "kolm models info google/gemma-3n-E2B-it" ], "customization_dimensions": [ "model-provider", "compute-target", "artifact-runtime", "proof-mode" ], "evidence_paths": [ "src/models.js", "src/model-registry.js", "src/model-weights-manifest.js", "public/account/train.html", "tests/wave409rs-models-devices.test.js" ], "happy_path": [ "inspect model catalog", "recommend by task/device", "check license and modality", "bind to compile or distill spec", "verify runtime target fit" ], "id": "models-backbones", "name": "Models And Backbones", "next_actions": [ { "href": "/account/train", "kind": "account", "label": "Pick the best backbone for the task and target device before starting a distill or compile.", "priority": "P0", "value": "/account/train" }, { "kind": "command", "label": "Run `kolm models recommend --json` or open the model catalog.", "priority": "P1", "value": "kolm models list --json" } ], "proof_commands": [ "kolm models list --json", "kolm models recommend --json", "kolm models info google/gemma-3n-E2B-it", "kolm devices recommend --json" ], "stage": "choose", "status_fields": [ "recommended_model", "family", "modalities", "license", "vram_gb", "device_fit" ], "surface": "models-backbones", "tui": [ "models", "devices", "compile" ], "user_story": "As a builder, I can choose real backbones such as Gemma 3, Gemma 3n, MedGemma, EmbeddingGemma, Qwen, Phi, Llama, Mistral, and SmolLM by task, license, modality, memory, and target device.", "ux_contract": [ "Gemma rows explicit", "license/device tradeoff visible", "no fake model families", "runtime target fit shown" ] }, { "account": [ "/account/train", "/account/overview" ], "api": [ "GET /v1/multimodal/tokenize/doctor", "POST /v1/multimodal/tokenize", "POST /v1/capture/media", "POST /v1/multimodal/bakeoff" ], "cli": [ "kolm media tokenize --path ./scan.png --json", "kolm media tokenize --dir ./evidence --json", "kolm media redact-job --path ./scan.pdf --json", "kolm bakeoff multimodal --json" ], "customization_dimensions": [ "privacy-mode", "model-provider", "storage-plane", "proof-mode" ], "evidence_paths": [ "services/embed/multimodal.js", "public/account/train.html", "tests/wave466-multimodal-bakeoff.test.js" ], "happy_path": [ "detect modality", "emit local feature sidecar", "capture media event", "review generated rows", "run multimodal bakeoff" ], "id": "multimodal-tokenization", "name": "Multimodal Tokenization", "next_actions": [ { "href": "/account/train", "kind": "account", "label": "Tokenize media into sidecars, then review/bake off the generated examples before training.", "priority": "P0", "value": "/account/train" }, { "kind": "command", "label": "Run `kolm media tokenize --path --json` on a local file.", "priority": "P1", "value": "kolm media tokenize --path ./scan.png --json" } ], "proof_commands": [ "kolm media tokenize --path ./scan.png --json", "kolm media tokenize --dir ./evidence --json", "kolm bakeoff multimodal --json" ], "stage": "prepare", "status_fields": [ "modality", "sidecar_path", "feature_tokenizer", "captioner", "transcriber", "errors" ], "surface": "multimodal-tokenization", "tui": [ "multimodal-tokenize", "multimodal-bakeoff", "datasets", "multimodal-pipeline" ], "user_story": "As a multimodal user, I can tokenize images, audio, video, PDFs, text, and code into searchable Markdown sidecars with local feature tokens and optional captions or transcripts.", "ux_contract": [ "local fallback sidecars", "optional provider captions", "no placeholder sidecars", "compile-ready provenance" ] }, { "account": [ "/account/overview", "/account/train" ], "api": [ "GET /v1/artifacts", "GET /v1/artifacts/:id", "GET /v1/artifacts/:id/download", "POST /v1/artifacts/dependency-graph" ], "cli": [ "kolm compile --spec spec.json --out task.kolm", "kolm quantize oracle --json", "kolm verify task.kolm", "kolm diff old.kolm new.kolm", "kolm export task.kolm --target gguf --preview", "kolm evidence format-governance --summary" ], "customization_dimensions": [ "artifact-runtime", "proof-mode", "storage-plane" ], "evidence_paths": [ "src/compile-pipeline.js", "src/quantization-oracle.js", "src/artifact.js", "src/format-governance-packet.js", "scripts/format-governance-packet.mjs", "docs/kolm-format-v1.md", "public/account/overview.html", "public/verify.html" ], "happy_path": [ "compile", "verify", "inspect", "diff", "export target" ], "id": "compile-verify", "name": "Compile And Verify", "next_actions": [ { "href": "/account/overview", "kind": "account", "label": "Compile, choose quantization/runtime strategy, verify, and export an artifact with receipts.", "priority": "P0", "value": "/account/overview" }, { "kind": "command", "label": "Run `kolm build ` to create a first artifact.", "priority": "P1", "value": "kolm compile --spec spec.json --out task.kolm" } ], "proof_commands": [ "kolm compile --spec spec.json --out task.kolm", "kolm quantize oracle --json", "kolm verify task.kolm", "kolm export task.kolm --target gguf --preview", "kolm evidence format-governance --summary" ], "stage": "compile", "status_fields": [ "artifact_count", "latest_k_score", "signature_state", "runtime_targets" ], "surface": "compile-verify", "tui": [ "artifacts", "compile", "audit-log" ], "user_story": "As an operator, I can compile a task into a signed .kolm artifact, verify it offline, diff versions, and export runtime-specific assets.", "ux_contract": [ "download and verify are adjacent", "manifest details expandable", "version diff visible", "no unverifiable marketing claim" ] }, { "account": [ "/account/overview" ], "api": [ "POST /v1/chat/completions", "GET /v1/devices", "GET /v1/devices/recommend", "GET /v1/streaming/capabilities" ], "cli": [ "kolm run task.kolm \"input\"", "kolm serve --mcp --http", "kolm chat-tui --model=kolm:task", "kolm runtime targets", "kolm packages release-readiness --summary", "kolm evidence runtime-adoption --summary" ], "customization_dimensions": [ "model-provider", "artifact-runtime", "deployment-mode", "compute-target" ], "evidence_paths": [ "src/completions-api.js", "src/runtime-policy.js", "src/package-release-readiness.js", "src/runtime-adoption-packets.js", "scripts/package-release-readiness.mjs", "scripts/runtime-adoption-packets.mjs", "public/sdk.js", "public/account/overview.html", "public/runtimes.html" ], "happy_path": [ "select model", "select runtime", "run request", "stream result", "log receipt" ], "id": "runtime-inference", "name": "Runtime Inference", "next_actions": [ { "href": "/account/overview", "kind": "account", "label": "Select artifact, fallback chain, runtime target, and device class.", "priority": "P0", "value": "/account/overview" }, { "kind": "command", "label": "Open the artifact list or use `kolm chat-tui --model=openai:gpt-5` to compare hosted versus compiled output.", "priority": "P1", "value": "kolm run task.kolm \"input\"" } ], "proof_commands": [ "kolm run task.kolm \"input\"", "kolm serve --mcp --http", "kolm tui --views --json", "kolm packages release-readiness --summary", "kolm evidence runtime-adoption --summary" ], "stage": "run", "status_fields": [ "runtime_target", "fallback_used", "latency_p95", "device_fit" ], "surface": "runtime-inference", "tui": [ "live-calls", "artifacts", "devices", "k8s-readiness", "runtime-placement" ], "user_story": "As an app developer, I can run a compiled artifact locally, in the browser, on edge/server runtimes, or behind OpenAI-compatible fallback chains.", "ux_contract": [ "fallback chain visible", "streaming supported", "latency/cost shown", "local/hybrid switch explicit" ] }, { "account": [ "/account/train", "/account/overview" ], "api": [ "GET /v1/cloud/readiness", "GET /v1/storage/object-readiness", "GET /v1/cloud/deploy-targets", "POST /v1/cloud/deploy-plan", "GET /v1/byoc/targets", "POST /v1/byoc/deploy", "GET /v1/byoc/deployments", "GET /v1/sync/status", "GET /v1/devices/detect" ], "cli": [ "kolm cloud broker --json", "kolm cloud readiness --remote --json", "kolm cloud storage --json", "kolm cloud storage --provider cloudflare-r2-s3 --smoke --json", "kolm cloud targets --json", "kolm cloud deploy-plan --target cloudflare-workers --artifact ", "kolm compute pick --json", "kolm remote recommend", "kolm cloud deploy --target aws-nitro --artifact " ], "customization_dimensions": [ "compute-target", "storage-plane", "deployment-mode", "governance-mode" ], "evidence_paths": [ "src/platform-capabilities.js", "src/object-storage.js", "src/cloud-compute-broker.js", "src/remote-compute.js", "docs/cloud-product-readiness.md", "scripts/cloud-readiness.mjs", "scripts/cloud-compute-broker.mjs", "public/account/train.html" ], "happy_path": [ "run readiness", "pick storage", "pick GPU/train backend", "deploy BYOC", "verify attestation" ], "id": "compute-cloud", "name": "Compute And Cloud", "next_actions": [ { "href": "/account/train", "kind": "account", "label": "Run the cloud broker and readiness doctors to choose the cheapest compliant compute, storage, and deployment profile before launching work.", "priority": "P0", "value": "/account/train" }, { "kind": "command", "label": "Configure R2/S3/Supabase and a GPU provider env var, or add remote SSH.", "priority": "P1", "value": "kolm cloud broker --json" } ], "proof_commands": [ "kolm cloud broker --json", "kolm cloud readiness --json", "kolm cloud storage --json", "kolm cloud storage --provider local-artifacts --smoke --json", "kolm cloud readiness --remote --json", "GET /v1/cloud/readiness", "GET /v1/storage/object-readiness", "GET /v1/cloud/broker/catalog", "POST /v1/cloud/broker", "kolm cloud doctor", "kolm compute list --json", "kolm remote recommend --json" ], "stage": "deploy", "status_fields": [ "artifact_storage", "hosted_gpu", "managed_train", "deployment_profile", "byoc_target", "remote_ssh" ], "surface": "compute-cloud", "tui": [ "devices", "storage-sync", "settings", "savings-tracker" ], "user_story": "As a team with no GPU, I can configure R2-compatible object storage, S3-compatible storage, AWS, Supabase, Modal, RunPod, Lambda, Together, or remote SSH and still build artifacts.", "ux_contract": [ "no secrets displayed", "missing vars grouped by provider", "local fallback shown", "cloud cost/capability surfaced before action" ] }, { "account": [ "/account/overview", "/account/dashboard" ], "api": [ "GET /v1/devices", "GET /v1/devices/detect", "POST /v1/devices/:id/register", "POST /v1/devices/:id/test", "POST /v1/devices/:id/install", "GET /v1/devices/recommend", "POST /v1/tunnel/register", "GET /v1/tunnels" ], "cli": [ "kolm devices detect --json", "kolm devices recommend --json", "kolm tunnel new --team ", "kolm install-device artifact.kolm --device ", "kolm airgap verify artifact.kolm" ], "customization_dimensions": [ "compute-target", "artifact-runtime", "deployment-mode" ], "evidence_paths": [ "src/device-capabilities.js", "src/router.js", "public/account/overview.html", 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user, I can detect devices, choose compatible runtimes, create team reverse tunnels, test installability, and push artifacts to local, SSH, HTTP, or air-gapped targets.", "ux_contract": [ "device class labels", "install test before deploy", "offline path visible", "runtime target warnings", "team tunnel path visible" ] }, { "account": [ "/account/org", "/account/api-control-center", "/account-billing" ], "api": [ "GET /v1/account", "GET /v1/account/keys", "POST /v1/account/keys", "GET /v1/account/audit-log", "GET /v1/billing/usage", "GET /v1/account/settings", "GET /v1/account/compliance-package", "POST /v1/capture/rbac/evaluate" ], "cli": [ "kolm whoami --json", "kolm keys list", "kolm audit --json", "kolm billing usage --json", "kolm team members", "kolm evidence compliance-certification --summary" ], "customization_dimensions": [ "governance-mode", "privacy-mode", "storage-plane", "proof-mode" ], "evidence_paths": [ "src/auth.js", "src/keys.js", "src/audit.js", 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"evidence_paths": [ "cli/kolm.js", "tests/wave298-doctor-loop.test.js" ], "group_id": "developer-experience", "id": "doctor-diagnostics", "priority": "P0", "status": "shipped", "title": "`kolm doctor` diagnoses environment, keys, toolchain, project, and K-score issues." }, { "claim_scope": "package-release-gated", "evidence_paths": [ "scripts/install.ps1", "packages/homebrew", "packages/apt", "packages/winget", "src/package-release-readiness.js", "scripts/package-release-readiness.mjs", "docs/package-release-readiness.md" ], "group_id": "developer-experience", "id": "one-line-install", "priority": "P1", "status": "needs_package_release", "title": "Install scripts exist; public package-manager channels need release-backed publication." } ] } ], "references": { "journey_research": [ { "id": "openai-distillation-evals", "url": "https://openai.com/index/api-model-distillation/", "why_it_matters": "Modern distillation products combine captured completions, evals, and fine-tuning; Kolm must add provider-independent artifacts and local/runtime proof." }, { "id": "anthropic-messages-api", "url": "https://docs.anthropic.com/en/api/overview", "why_it_matters": "Claude is a native provider surface, not an afterthought behind OpenAI-compatible routing." }, { "id": "cloudflare-ai-gateway", "url": "https://developers.cloudflare.com/ai-gateway/", "why_it_matters": "Gateway table stakes now include analytics, logging, caching, rate limiting, retries, and fallback." }, { "id": "vercel-ai-gateway", "url": "https://vercel.com/docs/ai-gateway/", "why_it_matters": "Unified APIs, budgets, usage monitoring, load balancing, and fallbacks are baseline UX for AI gateways." }, { "id": "opentelemetry-genai", "url": "https://opentelemetry.io/docs/specs/semconv/gen-ai/", "why_it_matters": "Production AI observability should use common GenAI spans, events, metrics, and provider attributes where possible." }, { "id": "vllm-openai-compatible", "url": "https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html", "why_it_matters": "Self-hosted serving engines increasingly expose OpenAI-compatible APIs; Kolm should route to them and compile away repeated work." }, { "id": "onnx-runtime", "url": "https://onnxruntime.ai/docs", "why_it_matters": "Portable inference has to target established runtimes and execution providers, not a proprietary-only runtime." }, { "id": "executorch-edge", "url": "https://docs.pytorch.org/get-started/executorch/", "why_it_matters": "Edge/mobile deployment needs device-aware export targets for constrained hardware." }, { "id": "workos-enterprise", "url": "https://workos.com/docs", "why_it_matters": "Enterprise buyers expect SSO, SCIM, audit logs, admin workflows, authorization, and secure vault patterns." } ], "surface_research": [ { "category": "provider-model-customization", "id": "openai-finetune", "state_of_art_signal": "Provider-hosted fine-tuning can create adapted models but remains provider-bound; Kolm must win on portability, receipts, local execution, and cross-runtime packaging.", "title": "OpenAI supervised fine-tuning", "url": "https://platform.openai.com/docs/guides/supervised-fine-tuning" }, { "category": "gateway-runtime-optimization", "id": "openai-prompt-caching", "state_of_art_signal": "Provider caching reduces repeated prompt cost; Kolm must prove avoided calls and deterministic local hot paths, not just cache discounts.", "title": "OpenAI prompt caching", "url": "https://platform.openai.com/docs/guides/prompt-caching" }, { "category": "gateway-runtime-optimization", "id": "anthropic-prompt-caching", "state_of_art_signal": "Prompt-cache block control is now standard for long-context apps; Kolm routing should preserve provider cache fallbacks while compiling stable repeated tasks.", "title": "Anthropic prompt caching", "url": "https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching" }, { "category": "observability-evaluation", "id": "langsmith-evals", "state_of_art_signal": "Datasets, experiments, traces, and online evaluators are table stakes; Kolm should import these signals and bind them into K-score evidence.", "title": "LangSmith evaluation concepts", "url": "https://docs.langchain.com/langsmith/evaluation-concepts" }, { "category": "observability-evaluation", "id": "langsmith-observability", "state_of_art_signal": "Trace every call, step, and decision; Kolm receipts and trace export must interoperate with this workflow.", "title": "LangSmith observability", "url": "https://docs.langchain.com/langsmith/observability" }, { "category": "observability-evaluation", "id": "arize-phoenix", "state_of_art_signal": "OpenTelemetry-based traces and eval integrations are standard; Kolm should emit and ingest OpenTelemetry/OpenInference-compatible traces.", "title": "Arize Phoenix", "url": "https://arize.com/docs/phoenix" }, { "category": "observability-evaluation", "id": "wandb-weave", "state_of_art_signal": "Tracing, evaluation, and improvement loops are bundled in modern LLM platforms; Kolm must bind improvements to portable artifacts.", "title": "W&B Weave", "url": "https://docs.wandb.ai/weave/" }, { "category": "model-customization", "id": "predibase-finetune", "state_of_art_signal": "LoRA fine-tuning and multi-adapter serving are mature; Kolm should treat training backends as pluggable while owning artifact proof and runtime packaging.", "title": "Predibase fine-tuning overview", "url": "https://docs.predibase.com/fine-tuning/overview" }, { "category": "model-customization", "id": "together-finetune", "state_of_art_signal": "Managed LoRA and full fine-tuning are widely available; Kolm should not compete on generic training UX alone.", "title": "Together AI fine-tuning", "url": "https://docs.together.ai/docs/fine-tuning-overview" }, { "category": "model-customization", "id": "openpipe-finetune", "state_of_art_signal": "Trace-to-fine-tune workflows are an adjacent wedge; Kolm must make trace imports and eval gates first-class.", "title": "OpenPipe fine-tuning quick start", "url": "https://docs.openpipe.ai/features/fine-tuning/quick-start" }, { "category": "gateway-runtime-optimization", "id": "openrouter-gateway", "state_of_art_signal": "Unified model access, routing, provider controls, and cost visibility are now a user expectation; Kolm's proxy must collect the same telemetry while preserving customer-owned provider keys and compiling repeated work into local artifacts.", "title": "OpenRouter API and model routing docs", "url": "https://openrouter.ai/docs/api-reference/overview" }, { "category": "gateway-runtime-optimization", "id": "litellm-gateway", "state_of_art_signal": "OpenAI-compatible proxy, spend tracking, fallbacks, and logging integrations are table stakes; Kolm must go beyond gateway mechanics by turning traces into signed local artifacts.", "title": "LiteLLM Gateway", "url": "https://www.litellm.ai/" }, { "category": "gateway-runtime-optimization", "id": "portkey-gateway", "state_of_art_signal": "Production AI gateways ship cache, retries, fallbacks, load balancing, budget limits, guardrails, and multimodal routing; Kolm must keep these compatible while adding private compile and verification.", "title": "Portkey AI Gateway", "url": "https://portkey-docs.mintlify.dev/docs/product/ai-gateway" }, { "category": "observability-evaluation", "id": "langfuse-observability", "state_of_art_signal": "LLM platforms now expose full traces, sessions, datasets, experiments, and online evaluations; Kolm's lake and opportunity engine must preserve trace fidelity and emit portable evidence.", "title": "Langfuse observability overview", "url": "https://langfuse.com/docs/observability/overview" }, { "category": "observability-evaluation", "id": "helicone-observability", "state_of_art_signal": "One-line proxy observability is a commodity surface; Kolm's capture path must remain drop-in while adding local-first ownership and compile-ready datasets.", "title": "Helicone AI gateway and observability", "url": "https://github.com/Helicone/helicone" }, { "category": "model-serving", "id": "lorax-serving", "state_of_art_signal": "Serving thousands of LoRA adapters on one GPU is mature infrastructure; Kolm should treat multi-LoRA serving as a downstream target, not the unique moat.", "title": "Predibase LoRAX multi-LoRA server", "url": "https://github.com/predibase/lorax" }, { "category": "model-serving", "id": "fireworks-lora-serving", "state_of_art_signal": "Cloud training platforms now support imported LoRAs, live merge, and multi-LoRA deployment; Kolm must preserve export provenance and verify artifacts across those hosted runtimes.", "title": "Fireworks LoRA deployment docs", "url": "https://docs.fireworks.ai/fine-tuning/deploying-loras" }, { "category": "model-customization", "id": "arcee-distillation-slm", "state_of_art_signal": "Small-model training and distillation are a competitive category, not a unique feature; Kolm should win on data capture, receipts, artifact portability, and local deployment.", "title": "Arcee Small Language Models", "url": "https://docs.arcee.ai/arcee-conductor/arcee-small-language-models" }, { "category": "edge-runtime", "id": "google-gemma-3n", "state_of_art_signal": "Phone-class multimodal backbones raise the minimum edge story; Kolm must recommend Gemma 3n where device budgets fit and record runtime_target/device_fit evidence.", "title": "Gemma 3n model overview", "url": "https://ai.google.dev/gemma/docs/gemma-3n" }, { "category": "model-customization", "id": "aws-bedrock-custom-models", "state_of_art_signal": "Managed distillation and fine-tuning are cloud-native enterprise expectations; Kolm should support BYOC/backend adapters and stronger artifact governance.", "title": "Amazon Bedrock model customization", "url": "https://docs.aws.amazon.com/bedrock/latest/userguide/custom-models.html" }, { "category": "identity-access", "id": "workos-identity", "state_of_art_signal": "SSO, SCIM, admin portal, and audit logs are expected for enterprise identity; Kolm account/team surfaces must converge there.", "title": "WorkOS docs", "url": "https://workos.com/docs" }, { "category": "identity-access", "id": "auth0-organizations", "state_of_art_signal": "B2B org modeling is a product primitive; Kolm must separate org, workspace, user, and tenant semantics.", "title": "Auth0 Organizations", "url": "https://auth0.com/docs/organizations" }, { "category": "billing", "id": "stripe-billing", "state_of_art_signal": "Self-serve subscription, invoice, and customer portal flows are table stakes; Kolm must move beyond payment-link-only certification.", "title": "Stripe Billing", "url": "https://stripe.com/billing/features" }, { "category": "billing", "id": "stripe-portal", "state_of_art_signal": "Customer-managed billing is expected; Kolm account billing should expose portal and webhook-backed entitlement state.", "title": "Stripe customer portal", "url": "https://docs.stripe.com/billing/subscriptions/customer-portal" }, { "category": "edge-runtime", "id": "apple-coreml", "state_of_art_signal": "On-device runtime targets are platform-native; Kolm should emit/verifiably wrap runtime-specific assets instead of pretending to replace them.", "title": "Apple Core ML", "url": "https://developer.apple.com/documentation/CoreML" }, { "category": "edge-runtime", "id": "apple-foundation-models", "state_of_art_signal": "On-device foundation model APIs raise the offline bar; Kolm needs proof, evals, receipts, and governance above native model access.", "title": "Apple Foundation Models", "url": "https://developer.apple.com/documentation/FoundationModels" }, { "category": "edge-runtime", "id": "google-litert", "state_of_art_signal": "Android and edge deployment should target established runtimes; Kolm should be compiler/governance layer.", "title": "Google LiteRT", "url": "https://ai.google.dev/edge/litert/overview" }, { "category": "edge-runtime", "id": "onnx-runtime-mobile", "state_of_art_signal": "Cross-platform mobile inference requires model/runtime optimization; Kolm artifact manifests must name target runtime and proof.", "title": "ONNX Runtime Mobile", "url": "https://onnxruntime.ai/docs/tutorials/mobile/" }, { "category": "edge-runtime", "id": "executorch", "state_of_art_signal": "Edge PyTorch deployment is a real substrate; Kolm should target it as an export backend.", "title": "PyTorch ExecuTorch", "url": "https://docs.pytorch.org/executorch/stable/intro-overview.html" } ] }, "route_surfaces": [ { "claim_scope": "surface-certified", "code_paths": [ "src/auth.js", "src/oauth.js", "src/stripe.js", "src/teams.js", "src/router.js", "cli/kolm.js" ], "competitor_refs": [ "workos-identity", "auth0-organizations", "stripe-billing", "stripe-portal" ], "doc_paths": [ "docs/PRODUCT.md", "docs/research/auth-boundary-audit-2026-05-12.md", "docs/research/billing-plan-enforcement-audit-2026-05-12.md" ], "id": 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"safe", "path": "/v1/compliance/certification-packet/template" }, { "auth": "none", "checks": [ "json" ], "expect": [ 422 ], "id": "compliance-certification-validate-public", "method": "POST", "mode": "safe", "path": "/v1/compliance/certification-packet/validate" }, { "auth": "required", "checks": [ "json" ], "expect": [ 200 ], "id": "notifications-state-auth", "method": "GET", "mode": "safe", "path": "/v1/notifications/state" }, { "auth": "required", "checks": [ "json" ], "expect": [ 200 ], "id": "trace-providers-auth", "method": "GET", "mode": "safe", "path": "/v1/trace/translate/providers" }, { "auth": "required", "checks": [ "json" ], "expect": [ 200 ], "id": "account-compliance-package-auth", "method": "GET", "mode": "safe", "path": "/v1/account/compliance-package" }, { "auth": "required", "checks": [ "json" ], "expect": [ 200 ], "id": "privacy-scan-deep", "method": "POST", "mode": "deep", "path": "/v1/privacy/scan" }, { "auth": "required", "checks": [ "json" ], "expect": [ 201 ], "id": "trace-append-deep-auth", "method": "POST", "mode": "deep", "path": "/v1/trace/append" } ], "promise": "Operators can prove who did what, what data moved, what artifact ran, what privacy controls applied, and what compliance package can be exported.", "readiness_requirement_ids": [ "async-compile-webhooks", "capture-filtering", "compliance-certifications", "fallback-chains", "k-score-calibration", "openai-anthropic-gateway", "opentelemetry", "redaction-quality", "streaming", "webhooks-events", "zero-retention-mode" ], "route_groups": [ { "id": "admin", "routes": 12 }, { "id": "audit", "routes": 29 }, { "id": "privacy", "routes": 7 }, { "id": "notifications", "routes": 12 }, { "id": "trace", "routes": 10 }, { "id": "telemetry", "routes": 1 }, { "id": "metrics", "routes": 2 }, { "id": "lineage", "routes": 2 }, { "id": "concepts", "routes": 5 }, { "id": "agents", "routes": 6 }, { "id": "session", "routes": 2 }, { "id": "lead", "routes": 2 }, { "id": "loop", "routes": 1 }, { "id": 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"src/media-store.js", "src/store.js" ], "competitor_refs": [ "apple-coreml", "apple-foundation-models", "google-litert", "onnx-runtime-mobile", "executorch", "aws-bedrock-custom-models" ], "doc_paths": [ "docs/research/device-offline-browser-governance-audit-2026-05-12.md", "docs/research/deployment-readiness-truth-2026-05-12.md", "docs/research/sandbox-hardening-decision-2026-05-12.md" ], "id": "deployment-edge-federated", "name": "Deployment, Edge Devices, BYOC, Storage, Sync, Tunnel, And Federated Learning", "primary_paths": [ "/device", "/byoc", "/self-host", "/tunnels", "/v1/devices", "/v1/cloud/readiness", "/v1/byoc/deployments", "/v1/storage/object-readiness", "/v1/storage/config" ], "production_smoke": [ { "auth": "required", "checks": [ "json" ], "expect": [ 200 ], "id": "devices-auth", "method": "GET", "mode": "safe", "path": "/v1/devices" }, { "auth": "required", "checks": [ "json" ], "expect": [ 200 ], "id": "devices-detect", "method": "GET", "mode": "safe", "path": 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"method": "GET", "mode": "safe", "path": "/v1/cc/kinds" }, { "auth": "required", "checks": [ "json" ], "expect": [ 200 ], "id": "device-recommend-deep", "method": "POST", "mode": "deep", "path": "/v1/devices/recommend" } ], "promise": "Artifacts can move across cloud, BYOC, device, tunnel, sync, confidential-compute, and federated workflows without losing tenant boundaries or evidence.", "readiness_requirement_ids": [ "deploy-buttons", "event-lake-schema" ], "route_groups": [ { "id": "device", "routes": 4 }, { "id": "devices", "routes": 18 }, { "id": "capability", "routes": 2 }, { "id": "cc", "routes": 3 }, { "id": "fl", "routes": 4 }, { "id": "federated", "routes": 13 }, { "id": "byoc", "routes": 7 }, { "id": "cloud", "routes": 10 }, { "id": "storage", "routes": 4 }, { "id": "sync", "routes": 6 }, { "id": "tunnel", "routes": 3 }, { "id": "tunnels", "routes": 2 }, { "id": "airgap", "routes": 9 }, { "id": "region", "routes": 3 }, { "id": "residency", "routes": 4 }, { "id": "sneakernet", "routes": 2 }, { "id": "bundle", "routes": 1 }, { "id": "deploy", "routes": 6 }, { "id": "test-device", "routes": 1 }, { "id": "fleet", "routes": 5 } ], "routes": 107, "status": "certified" } ], "schema": "kolm-product-graph-1", "source_docs": { "product_experience": "src/product-experience.js", "product_journeys": "docs/product-journeys.json", "product_sota_readiness": "docs/product-sota-readiness.json", "product_surfaces": "docs/product-surfaces.json" } }