services
What We Build
Four service areas, all focused on production. Built for startups that need to move fast without accumulating debt that comes back to bite them.
The four engagements
RAG & Retrieval Systems
Search, Q&A, and document intelligence that stays accurate at scale.
Who this is for
Startups building search, Q&A, document intelligence, or any product where an LLM needs to answer questions from a knowledge base.
What we deliver
- Document ingestion pipeline (chunking, embedding, vector store)
- Hybrid search implementation (semantic + keyword)
- Re-ranking layer for retrieval precision
- Generation with source citation and hallucination controls
- Evaluation harness with recall and accuracy benchmarks
- Observability: query tracing, latency monitoring, cost tracking
// pipeline shape
query → embed → vector + BM25
→ rerank → context window
→ LLM → scored answer
recall & faithfulness tracked per release
Multi-Agent Systems
Multi-step work that recovers from its own failures.
Who this is for
Startups building autonomous workflows, research pipelines, or multi-step AI processes that require coordination across tasks.
What we deliver
- Agent architecture design (supervisor, sequential, or parallel patterns)
- Tool integration and validation
- State management and memory across agent runs
- Error recovery, retry logic, and graceful degradation
- Monitoring and observability for agent behavior
// run trace
planner → researcher → writer
tool call failed → retried 1/3 ✓
state checkpointed
every step replayable after the fact
LLM Infrastructure
The backend your AI feature runs on, priced so it can scale.
Who this is for
Startups with LLM features in production who are hitting performance, cost, or reliability issues at scale.
What we deliver
- Inference cost optimization (caching, batching, model routing)
- Latency optimization for real-time and streaming use cases
- Rate limiting, circuit breakers, and failover for LLM API calls
- Token budget management and cost controls
- Scalable async job queues for non-real-time LLM workloads
// after the rebuild
cache hit rate ......... 0.62
p95 latency ............ 1.4s
cost / 1k queries ...... $7.30
illustrative figures, not a client's
AI Architecture Consulting
One or two weeks of design work before anyone builds.
Who this is for
Teams about to start a major AI build who want to get the architecture right before committing to an implementation path.
What we deliver
- 2–4 week engagement
- Architecture document: system design, technology selection, data flow, integration strategy
- Risk identification: where the system is likely to fail and why
- Build plan: phased implementation roadmap with clear milestones
// deliverable
architecture.md
risks.md — ranked, with mitigations
evals/ — the bar to clear
yours to keep, build with us or not
What else we take on, and what we turn down.
Saying this up front saves everyone a call.
we do this
SaaS platform builds
We've built and run our own AI platform, so the multi-tenant, billing, and usage-metering problems aren't new to us.
we do this
Embedded AI engineers
When you need the expertise inside your own team, we place senior AI engineers alongside yours for a defined stretch.
we don't
Train models from scratch
Almost nobody needs it. If you genuinely do, we'll tell you plainly and point you to someone who does it well.
Not sure which one you need? That's the call.
Describe the problem in plain words. We'll tell you which engagement fits, or that none of them do.
Book a Discovery Call →// how pricing works
Review: Fixed fee, one to two weeks, deliverables listed above.
Build: Fixed price quoted against the architecture doc, paid in milestones.
After: Optional support, monthly, cancel whenever.
No open-ended retainers