AI that does real work — not demos.
We turn language models into reliable systems that take action, retrieve truth from your data and reason over your business. From a support agent that resolves tickets to a document pipeline that reads thousands of pages a day.
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Five AI capabilities, production-grade.
Generative AI & LLM Integration
Custom apps on GPT, Claude, Gemini and open models (Llama, Mistral) — with finetuning, prompt engineering, evaluation suites and cost controls so quality holds in production.
RAG Pipelines & Vector Search
AI that answers from your data with citations. Pinecone, Milvus, Qdrant and pgvector, with chunking, reranking and freshness pipelines tuned for your content.
AI Agents & Multi-Agent Workflows
Autonomous agents that plan, call tools and complete multi-step tasks — MCP servers, n8n and Make integrations, with guardrails, approvals and full audit trails.
Computer Vision & OCR
Document parsing pipelines, invoice and ID extraction, defect detection and image classification — deployed on cloud or edge with human-in-the-loop review.
Voice AI & Predictive Scoring
Speech-to-text and voice assistants, plus risk engines and predictive models (churn, fraud, lead scoring) wired directly into your product and CRM.
Business Process Automation
End-to-end automations that connect your tools — email, sheets, CRMs, ERPs — with AI in the loop where judgment is needed and humans where it matters.
Every AI engagement includes
- Model selection & cost/latency benchmarking
- Evaluation suite with regression tests
- Guardrails, moderation & PII redaction
- Observability: traces, token costs, quality dashboards
- Deployment on your cloud or ours
- Documentation & team handover training
Stack we reach for
AI questions, answered
We build with GPT, Claude, Gemini and open models like Llama and Mistral — hosted or self-hosted. We pick the model per use case based on quality, latency, privacy and cost, and design so you can swap models later without a rebuild.
RAG (retrieval-augmented generation) connects an AI model to your own documents and data so answers are accurate and cite sources. If you want AI that knows your business — support bots, internal search, document Q&A — RAG is usually the right architecture.
A working proof of concept typically ships in 2–3 weeks. Production hardening — evaluation, guardrails, monitoring and scaling — usually takes another 3–6 weeks depending on scope.
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