RAG & retrieval systems
Hybrid retrieval (BM25 + vector + reranker), chunking strategies that respect document semantics, citation tracking. We've learned the hard way which retrieval failures look like model failures.
Services / AI & Data
Make AI a line in your roadmap, not a side project.
Most AI work stalls in pilot. Ours ships. We build retrieval, evaluation, and analytics systems that earn their keep on day one — and stay accountable for the next eighteen months.
12+
Production AI systems shipped
9 wks
Median time-to-first-value
4×
Cost reduction vs. naïve LLM use
0.94
Median F1 on customer eval sets
What it is
The work, plainly described.
AI & Data Solutions is the practice that takes a fuzzy AI ambition — "do something with our docs," "replace this manual review," "surface insight from these tickets" — and ends with code in production, evals in CI, and a team that knows how to run it. We do the boring, load-bearing parts most teams skip: retrieval that actually retrieves, evals that catch regressions, latency budgets that hold under traffic.
Where it fits
- Series B/C SaaSProduct team needs AI features that ship and don't hallucinate at the demo. We build the retrieval, the guardrails, and the eval harness.
- Mid-market enterpriseYou've spent twelve months on a copilot that nobody uses. We help you triage what to keep, what to rebuild, and what to retire.
- Regulated industriesHealthcare, finance, government — where every output needs a citation, an audit trail, and a fallback. We've built for HIPAA, SOC 2, FedRAMP-aware contexts.
- Founders shipping v1You don't need a research lab. You need a working product. We get to deployable in eight to twelve weeks with a real eval set.
Capabilities
What we'll actually do.
Each of these is a deliverable category, not a buzzword bullet. We scope, build, and stay accountable for each one.
Document intelligence
Extraction pipelines for contracts, claims, SOWs, clinical notes, and regulatory filings. Layout-aware models, table extraction, structured output validation.
Agentic workflows
Tool-using agents with bounded action spaces, dry-run modes, and human-in-the-loop checkpoints. We design for failure modes first, capability second.
LLM evaluation & CI
Eval suites you can run on every PR, regression detection across model upgrades, A/B harnesses for prompt iteration. The thing your AI roadmap is missing.
Analytics & data platforms
Modern stack — Snowflake, Databricks, BigQuery — with semantic layers, dbt models, and dashboards your executives actually open.
Privacy & safe AI
PII detection, redaction, on-prem and air-gapped deployments, prompt injection defense. Compliance-aware from day one.
Process
How an engagement actually runs.
No mystery, no shifting goalposts. Five phases with measurable outcomes per phase.
Discovery sprint
Two weeks. We map the problem space, run technical spikes, and produce a build plan with cost, risk, and a real eval set.
Foundation build
Weeks 3-6. Retrieval, eval harness, observability, and a working prototype your team can break.
Production hardening
Weeks 7-10. Latency, cost, fallback paths, prompt injection defense, on-call playbooks.
Pilot deployment
Weeks 11-12. Shadow traffic, then a real cohort. Live metrics, weekly review, fast iteration.
Sustained delivery
Month 4 onward. Monthly model upgrades, eval expansion, and embedded engineers if you want them.
Why us
Three things you should know.
We ship to production, not to slides
Every engagement targets a deployed system with monitoring, evals, and a runbook. "Demo-ready" isn't a goal we accept.
Eval-first is non-negotiable
We won't build features without a measurable acceptance test. If we can't evaluate it, we'll tell you why and propose what would make it measurable.
Senior engineers do the senior work
No bait-and-switch staffing. The architect on the proposal is the architect on your project.
Frequently asked
The questions everyone asks.
How is this different from hiring an LLM consultant?
Most LLM consultants stop at a working prototype. We commit to deployment, on-call, and the boring infrastructure work that determines whether your AI feature is still working in six months.
Do you work with our existing models, or do we have to use yours?
We're model-agnostic and provider-agnostic. We've built on OpenAI, Anthropic, Bedrock, Vertex, Azure OpenAI, and self-hosted Llama and Mistral. We pick what fits your latency, cost, and compliance constraints.
Can you guarantee no hallucinations?
No, and you should be skeptical of anyone who does. What we can guarantee is a citation-and-evidence pattern, eval-driven regression catching, and human-in-the-loop checkpoints that keep error rates inside your tolerance.
Do you do model fine-tuning?
When it's the right answer. Most of the time, better retrieval, better prompts, and better evaluation outperform fine-tuning. We'll tell you when fine-tuning earns its weight.
What does the engagement cost?
Discovery sprints run $24-48k. Most full engagements run $180k-$450k for the first three months. We share a detailed estimate after the discovery sprint, not before.