AI Integration & Chatbots
LLM-powered products and intelligent agents.
We build production AI features — RAG copilots, autonomous agents, intelligent classifiers, multimodal pipelines — for companies that need them to work, every time, at sane cost.
Inside a real RAG pipeline.
AI integration that actually ships.
Most AI projects die at the demo stage. Ours don't.
The notebook works. The investor deck looks great. But the path from prototype to a production system users can rely on — without hallucinating, leaking data, or burning the cloud budget — is where most teams stall.
We ship AI products to real production. Support copilots, outbound agents, document intelligence, AI-augmented search. The model is one layer in a real system that needs retrieval, evaluation, observability, cost control, and safety.
RAG is a system, not a prompt.
Naive RAG looks easy in a tutorial. In production, every step has a subtle failure mode — and they compound.
Bad chunking destroys retrieval precision. Missing reranking returns wrong sources. Naive prompts hallucinate. No evaluation means a silent model upgrade can break your accuracy.
- fixed-size chunking
- single embedding recall
- no reranking stage
- unstructured text output
- no evaluation suite
- silent regressions
- metadata-aware chunking
- hybrid dense + BM25 + filters
- Cohere / Jina reranking
- structured outputs + citations
- golden-set eval on every PR
- regression alerts in Slack
Agents that actually do work.
Tool-using agents are powerful — if they're reliable. Reliability is the whole engineering problem.
We scope the toolset narrowly, wrap every call in retry / timeout / circuit-breaker logic, use structured outputs at every system boundary, and add explicit human-in-the-loop checkpoints for irreversible actions.
Costs, latency, and the stuff nobody puts on a slide.
A poorly designed AI system burns $50K/month. A well-designed one delivers the same UX for $500.
The difference is engineering. Prompt caching, model routing, batch APIs, context pruning, embedding caches, and aggressive instrumentation.
1// Route cheap queries to Haiku, hard ones to Opus2const model = router.pick({3 complexity: await classify(input),4 context_length: input.length,5 budget_tier: user.plan,6})78const response = await llm.complete({9 model,10 system: cached(SYSTEM_PROMPT),11 messages,12 max_tokens: budget.tokens,13})1415await metrics.emit(response.usage)
Safety, evaluation, and the boring stuff that matters.
Shipping AI without evaluation is shipping a database without monitoring.
Every AI feature we ship includes a real eval harness: golden-set tests, LLM-as-judge rubrics, regression alerts, and a dashboard your PMs can read.
- Golden-set evaluation suite
- LLM-as-judge scoring rubrics
- Prompt injection input validation
- Output policy filtering
- Citation enforcement for factual claims
- Per-user spend + rate limits
- HIPAA / SOC 2 / PCI-aligned controls
- Cost & latency observability dashboards
What we ship in this practice.
Customer-support copilot
RAG-grounded assistant on your help center + ticket history + product docs. Cuts ticket resolution 40–60%, deflects ~30% of tier-1.
Clinical chart summarizer
Longitudinal record summaries for clinicians. PHI-safe pipeline, audit trail, HIPAA-aligned.
Outbound sales agent
Tool-using agent that researches prospects, drafts personalized outreach, books meetings. CRM integrations included.
Document intelligence
Contract / invoice / claims extraction at scale. Vision LLMs + structured outputs. 95%+ accuracy on golden sets.
AI-powered search
Replace keyword search with semantic + reranked retrieval. Auto-evaluated on your real query log. CTR up 2–4x in pilots.
Voice + multimodal
Real-time voice agents with sub-500ms turn-taking, vision-aware screen assistants, image-to-spec extraction.
Pick the right model.
Idea → Roadmap → Product → Release.
Every engagement runs through the same four-stage pipeline. Predictable by design.
Tools we trust.
AI Products for your context.
Tailored entry points by industry vertical or US metro - each page is hand-tuned with the right keywords, compliance, and case studies.