From CrewAI multi-agent systems to custom MCP implementations — we engineer AI that ships to production. Document parsing, vector search, RAG pipelines and LLM orchestration tuned for domain-heavy work.
Multi-agent orchestration with CrewAI, custom MCP server implementations, and tool-using LLM agents — built on Claude, GPT, Llama, Mistral or Azure OpenAI, whichever fits your latency, cost and residency needs.
Retrieval-augmented generation tuned to your domain — hybrid search, reranking, semantic chunking, and an eval harness that catches regressions.
Classical ML where it earns its keep — forecasting, classification, ranking — with proper feature stores, training pipelines and online inference.
Document understanding, OCR pipelines, defect detection, video analytics — on-device or in your cloud, with active-learning loops.
We do not ship an AI feature without an automated eval suite and a rollback plan.
What does "good" look like? Pick metrics before models.
Golden datasets, rubrics, and automatic scorers.
Model, retrieval, tools — chosen for cost + latency + accuracy.
Daily eval runs. Every prompt change is a tracked experiment.
Production traces, drift alarms, human review queues.
We design every system so you can switch model providers in a day, not a quarter. No vendor lock-in by accident.
Real-time document-tampering detection. 10M+ documents in training; X-ray-vision metadata forensics.
Deep-learning bank-statement analytics on 50M+ transactions, 40+ categories, multi-language.
Automated employee reference-verification platform — NLP engine that collects, analyses and summarises qualitative feedback into structured candidate scores.
End-to-end AI + big-data + automation platform across the drug-development lifecycle.
Enterprise ad-matching engine combining LLMs with Retrieval-Augmented Generation pipelines.
NLP-driven automated employee reference verification with scored summaries and recruiter dashboards.
Most AI pilots take 6–10 weeks from kickoff to a usable v1 in your environment, with a clear go/no-go decision at the end.
Your cloud. We deploy AI workloads in your AWS / GCP / Azure account using your keys — so data, logs and audit trails never leave your perimeter.
We use citation-first generation, guarded tool use, and an automated eval suite that catches regressions before they ship. For regulated workflows, every model output is reviewable.
Yes — we fine-tune open-weight models when there's a clear cost / quality win. But for most use cases, well-built prompts + retrieval beat fine-tuning. We'll tell you which one applies.
Three engagement models — fixed-cost project, pay-per-seat dedicated team, or bespoke commercials for non-standard work. Every quote is scoped to your project; we share commercials only after a discovery conversation.
Tell us the problem. We'll tell you if it's worth solving with AI.
Scope an AI Project