We integrate practical AI — LLMs, RAG pipelines, agents, and automation — where it creates real value for your users, not just your pitch deck.
2–4 wks
Per AI feature
GPT-4o+
Models we use
Eval-first
Our approach
Processing 2.4k docs
Accuracy: 97.3%
Building a product where AI is the core — chat interfaces, autonomous agents, intelligent workflows.
You have an existing product and want to add AI features without derailing your current roadmap.
Reducing manual work with AI — document processing, classification, routing, and intelligent notifications.
Context-aware chat interfaces powered by LLMs — trained on your product data, brand voice, and user workflows.
Semantic search over your docs, PDFs, and databases so users get instant, accurate answers.
AI agents that handle repetitive tasks — classification, routing, enrichment, and action-taking.
Extract, classify, and summarise unstructured documents at scale with structured output.
Let users query their data in plain English and get charts, summaries, and insights instantly.
Building AI products requires more than wrapping GPT. We handle the full pipeline — from data ingestion to production monitoring.
LLM Integration
OpenAI, Anthropic, Gemini — whichever fits your use case
Vector Store & RAG
Pinecone, pgvector — semantic search over your data
Chat Interface
Streaming responses, message history, context management
Evaluation & Testing
Automated evals to catch regressions before production
AI Agent Pipelines
Multi-step reasoning chains with tool use and memory
Prompt Engineering
System prompts, few-shot examples, and output structuring
Safety & Guardrails
Content filtering, hallucination detection, fallback handling
Monitoring & Cost Control
Token tracking, latency alerts, and usage dashboards
Most AI projects fail because nobody tested whether the output was actually good. We build evaluation in from day one.
We identify exactly where AI creates real value — not hype. Most projects need one well-scoped AI feature, not ten mediocre ones.
We design the data pipeline, vector store, prompt structure, and LLM integration before writing production code.
We build in tight loops with evaluation at every step — testing output quality, edge cases, and failure modes before shipping.
Production deployment with token cost tracking, latency monitoring, and a feedback loop for continuous improvement.
We don't ship AI features that hallucinate. Every build includes evaluation pipelines to catch failure modes before your users do.
Tech stack
AI is genuinely useful in the right places. We'll tell you when it's the right choice — and when a simpler rule-based approach gets you there faster with less cost and complexity. We build for outcomes, not for the sake of having "AI" in the product.
We work across OpenAI (GPT-4o), Anthropic (Claude), and Google (Gemini) — and help you choose the right model for your use case based on cost, latency, and capability tradeoffs.
We build evaluation pipelines alongside the product — automated test suites that run your prompts against expected outputs. This catches regressions before they reach users.
Yes — most of our AI work is integrating into existing products, not greenfield builds. We audit your current stack, identify the right integration points, and ship without disrupting what's working.
A well-scoped AI feature (RAG chatbot, document extraction, workflow automation) typically takes 2–4 weeks from kickoff to production. Full AI-first products take 4–8 weeks.
RAG is the right choice when users need answers grounded in your specific data — docs, knowledge base, product info. We'll tell you honestly if a simpler approach works better.
AI features start from $5k for a well-scoped integration. Full AI-first products typically start from $15k. We quote a fixed scope after discovery so there are no surprises.
Tell us what you're building. We'll scope the AI integration, pick the right models, and ship something that works reliably in production.