AI · Agent engineering

Production AI agents - built inhouse, not stitched together.

Custom LangChain agents. RAG over your documents. Multi-step workflow automation. Powered by GPT-4 / Claude / Gemini. Built for real production traffic - not a notebook demo. Founder writes the code. You own the model API keys, the prompts, the vector store.

LangChain · LangGraph · CrewAI · custom orchestration
RAG with Pinecone / Weaviate / pgvector + reranking
Function-calling, tool use, multi-agent orchestration
Observability + cost guardrails + eval harness from day one

Talk to the engineer who'll build it 15 min, no SDR

Agent · support triage

12k

Tickets/mo

68%

Auto-resolved

$0.04

Per ticket

4.6

CSAT

🤖

Intent classifiedrefund_request · 0.94 confidence

Auto

📚

RAG over policy docs3 docs retrieved + reranked

Active

↗

Escalated to humanEdge case · low confidence

Why purpose-built

Most "AI agents" are demos that broke in production.

Production-grade, not notebook

Streaming, retries, fallbacks, cost ceilings, circuit breakers, prompt versioning. Logs every token. Replay every conversation.

Eval harness from day one

Hand-graded eval set + Ragas / DeepEval / LangSmith hooked up. Every prompt change runs against fixtures. No "vibes-driven" prompting.

Your keys, your data

API keys live in your account. Vector store in your VPC. Customer data never leaves your boundary. SOC 2 / GDPR / DPDP friendly.

Six agent patterns we ship

Real workflows, real business impact.

Support triage agent

Classify intent, retrieve relevant policy docs, draft reply, escalate edge cases. 60-80% auto-resolution typical.

Sales / lead qualifier

Chat with leads, qualify against ICP, book meetings, push to your CRM. WhatsApp + web embed.

Document Q&A (RAG)

Ingest manuals / policies / contracts. Answer staff or customer questions with citations + source quotes.

Internal ops copilot

Slack / Teams bot that queries internal data (Postgres / Snowflake / Notion / Jira) on natural-language ask.

Underwriting / claims agent

OCR + parse documents, extract structured fields, score risk, draft decision letter - human reviews edge cases.

Multi-agent workflows

Researcher → Writer → Reviewer pipelines. LangGraph orchestration. Each agent specialised, supervised by an orchestrator.

Stack we use

Open-source where it matters, frontier models where it pays.

We don't religiously pick "all OSS" or "all GPT-4." We benchmark per task and pick what wins. Eval harness validates every decision.

LLMs: GPT-4o / Claude 4.5 / Gemini / Llama (fine-tunable)
Orchestration: LangChain · LangGraph · CrewAI
Vector: Pinecone · Weaviate · pgvector · Qdrant
Eval + observability: LangSmith · Ragas · DeepEval · Phoenix
Backend: Node · Python · FastAPI · Postgres

Agent observability · LangSmith

📊

p95 latency2.4s · within SLO

Good

💰

Cost/conversation$0.04 avg · $0.18 p99

Track

✅

Eval pass rate94% · 200 fixtures

Pass

FAQ

Things teams ask before signing.

How long to ship a production agent?

4–6 weeks for a production-ready agent handling one core workflow. Includes eval harness, observability, cost guardrails. Multi-agent or fine-tuning extends this 2–4 weeks.

What does it cost?

From $999 for a simple RAG / chatbot agent. $2,500–$8,000 for multi-step / multi-agent workflows. Plus your LLM API costs (paid directly to OpenAI / Anthropic - we don't markup).

Will it hallucinate?

Yes, sometimes. We mitigate with grounded retrieval (RAG with citations), confidence scoring, and escalation to humans for low-confidence cases. Hand-graded eval harness catches drift.

Can we use Llama / open-source models instead of GPT-4?

Yes - we benchmark per task. Some workflows run great on Llama 3 / Mistral. Some need frontier (GPT-4 / Claude). We pick what wins on eval, not what's trendy.

Send us your brief

Tell us the workflow. We'll send a real plan.

Support, ops, sales, underwriting - wherever you have a workflow that's chewing engineer / analyst time, an agent can probably take 60-80% of it. Send the workflow - we'll come back with a build plan, eval set, and an honest quote.

Send your brief Talk to an engineer · 15 min