PAIN · 0101 / 03
Match quality stalls at scale.
What worked at ten thousand seekers stops working at five hundred thousand. Embeddings drift, BM25 alone misses intent, and one bad reranker tanks a market.
↓ How we fix it, below.
★ Industry vertical · Recruiting AI06 service items8-week engagements
INDUSTRY · RECRUITING AI · CAREER & TALENT TECHCareer tech that survives the launch.
AI matching for the platforms that move millions of people into work. Vector retrieval, eval suites, multilingual career copilots, and the boring infrastructure that keeps a recommender running at half a million seekers.
Industry
Career tech · Recruiting AI
Scale
500k+ seekers benchmarked
Coverage
64 districts · 15 verticals
Stack
Direct LLM · no orchestration
[WHAT WE HEAR ON THE FIRST CALL]
Three pains every career platform hits.
We have watched these patterns in conglomerate-backed marketplaces, independent talent platforms, and university-affiliated career portals. The shape is the same.
The fix is not a better demo. The fix is the boring infrastructure underneath the demo. Eval gates, hybrid retrieval, cost meters, and the discipline to refuse a model that does not pass.
PAIN · 0202 / 03
AI reliability is a board conversation.
Conglomerate-backed platforms get asked about hallucination, fairness, and uptime in the same meeting as revenue. Without an eval bar, the answer is always a story.
↓ How we fix it, below.
PAIN · 0303 / 03
The architecture is being decided this quarter.
Early-stage platforms lock in vector stores, model providers, and retrieval shapes that they will live with for three years. The wrong call costs a rebuild, not a refactor.
↓ How we fix it, below.
[SIX SERVICE ITEMS · ONE TEAM]Pick the recruiting-AI problem.
Pick the recruiting-AI problem.
We'll bring the build.
Each item is a focused engagement: eight weeks, fixed scope, eval suite at handoff. Bundle two or three when the problem warrants.
SERVICE · 01 / 06Core product
AI matching engine
The retrieval + ranking stack your platform runs on. Built to survive the launch and the next million users.
- Hybrid BM25 + vector retrieval on Postgres
- LLM-scored final fit with explainability
- Cold-start strategies for new seekers and new JDs
PostgreSQLpgvectorOpenAICohere
SERVICE · 02 / 06AI reliability
Eval suite for matching
The number that turns 'AI reliability' from a board worry into an engineering metric. Gated on every release.
- Golden set of validated matches per market
- Relevance, diversity, fairness scoring
- A/B harness + drift detection on production traffic
LangSmithPromptfooTypeScriptPython
SERVICE · 03 / 06Multilingual RAG
Career guidance copilot
A career chat that holds context across multi-turn conversations and switches between English and the local language without losing the thread.
- RAG over career data, mentorship transcripts, campus content
- Mixed-language embeddings tuned for the market
- Prompt patterns that do not switch language mid-answer
AnthropicpgvectorTypeScriptOpenTelemetry
SERVICE · 04 / 06Employer side
Skill-based hiring tools
Resume parsing, skill graphs, and explainable ranking that gives recruiters a 'why this candidate' for every match.
- NER + skill taxonomy mapping from resumes and JDs
- Bias-aware ranking with audit logs
- Per-candidate 'why this fits' explanation surfaced to employers
PythonPostgreSQLOpenAIZod
SERVICE · 05 / 06Operate at scale
Production observability + cost
Cost-per-match metering, latency p95 per market, and quality-drift dashboards. The CFO sees the same numbers as the on-call.
- OpenTelemetry traces across retrieval + rerank + LLM call
- Cost-per-intent budgets per tenant or market
- Eval-as-monitor: regression alerts before the user notices
OpenTelemetryDatadogGrafanaSentry
SERVICE · 06 / 06Early-stage wedge
Architecture review
One-week audit of your retrieval stack, eval gate, model selection, and deployment shape. Written decisions you can defend to a board.
- Vector store: Postgres + pgvector or vendor SaaS
- Model + provider mix with exit costs scored
- Eval suite shape: what to build first, what to defer
ADRsPostgresvLLMCloudflare
Most engagements bundle two: a build (01, 03, 04) paired with the discipline that keeps it shipping (02, 05). Bring the shape closest to your blocker.
Scope your engagement →Want to see the K-Framework discipline behind every item? Read the K-Framework.
[THE STACK · BY LAYER]
Boring infrastructure. Production results.
We pick the simplest tool that survives the audit. Most of the time that means Postgres, the model API, and your existing infra.
LAYER · DATA + RETRIEVAL
Data + retrieval.
The store, the index, the search
PostgreSQLpgvectorRedisClickHouseBigQueryOpenSearch
LAYER · MODEL LAYER
Model layer.
Embeddings, providers, fallbacks
OpenAIAnthropicCohere EmbedVoyageLlama (self-hosted)vLLM
LAYER · EVAL + OBSERVABILITY
Eval + observability.
The eval bar, the cost meter, the drift alarm
LangSmithPromptfooOpenTelemetryDatadogGrafanaSentry
LAYER · BACKEND + TRANSPORT
Backend + transport.
Type-safe everything
TypeScriptNext.jsPythonFastAPIgRPCBullMQtRPCZod
LAYER · MOBILE
Mobile.
iOS + Android, native or cross
React NativeExpoSwiftKotlinFCMAPNs
LAYER · CLOUD + DEPLOYMENT
Cloud + deployment.
Whatever your infra already runs
Cloudflare WorkersCloudflare R2AWSGCPVercelFly
✕ WHAT WE DO NOT SHIP
Direct against the model API. The same way you integrate against Postgres.
- ✕ No LangChain
- ✕ No LlamaIndex
- ✕ No agent framework
- ✕ No orchestration vendor
- ✕ No black-box ML platform
[PROOF · WHAT THE STACK DELIVERS]Numbers that hold up
MEASURED · WEIGHTED · 2024–2026Numbers that hold up
in the second meeting.
SCALE
500k+
Seekers benchmarked on hybrid retrieval
REACH
64
Districts of Bangladesh coverage
BREADTH
15
Industry verticals in match graph
LATENCY
p95 / 200ms
Median match-API latency target
RECRUITING AI · APPLIED K-FRAMEWORKBring the matching problem.
Bring the matching problem.
We'll bring the build.
Eight weeks, fixed scope, eval suite at handoff. Direct LLM engineering on top of the K-Framework. Two Q3 slots remain.
CYCLE
8 weeks · problem to live
OUTPUT
Code · evals · runbook
LICENSE
Yours, MIT, no lock-in