We design and build AI systems that solve real business problems — not demos. From LLM integration to intelligent automation pipelines, we deploy AI that runs reliably at scale.
Production integration of large language models (OpenAI, Anthropic, Google Gemini, Mistral) into your existing applications and workflows. We handle prompt engineering, response validation, cost optimization, fallback logic, and the operational concerns that separate a demo from a deployed system.
Retrieval-Augmented Generation systems that let your AI work with your actual data — internal documents, knowledge bases, contracts, support histories. We design the chunking strategy, embedding pipeline, vector store, and retrieval logic that makes RAG accurate and fast.
Replacing manual, repetitive knowledge work with AI-powered pipelines. Document classification, data extraction, report generation, email triage, compliance checking — we identify the highest-value automation targets and build them into production workflows.
The cloud infrastructure that AI systems need to run reliably — GPU instance management, model serving, inference optimization, monitoring, and cost controls. We bring our cloud infrastructure expertise to AI deployments so you're not paying 10x what you should.
Full-stack AI feature development for SaaS products — from API design through frontend integration. We work alongside your engineering team to ship AI features that are explainable, testable, and aligned with your product roadmap.
A structured evaluation of where AI can create measurable value in your business — and where it can't. We assess your data, workflows, and technical infrastructure, then deliver a prioritized roadmap with realistic timelines and ROI estimates. This is where most AI projects should start.
A legal services firm reviewed 400+ contracts per month manually — each taking 45–90 minutes. We built a RAG-based system that extracts key clauses, flags non-standard terms, and surfaces risk factors, reducing first-pass review to under 10 minutes.
A B2B SaaS company with a 3-person support team was drowning in tier-1 tickets. We built an AI assistant trained on their documentation, past tickets, and product knowledge base that handles first-line resolution and escalates with full context.
An investment firm received 200+ PDF financial reports weekly and extracted key metrics manually into spreadsheets — a full-time job for two analysts. We built an LLM extraction pipeline that processes each report in under 90 seconds with structured output.
The best AI implementations we've seen started with a clear business problem, not "we should use AI." We spend the first phase of every engagement validating that AI is actually the right tool — and that the problem is specific enough to build something reliable around.
An AI system that works in a notebook is not an AI system. Production means monitoring, fallback handling, cost management, latency budgets, and behavior that's consistent enough to trust. We engineer for production from day one.
Generic AI gives generic results. The value comes from grounding systems in your specific data, your terminology, your edge cases. We invest heavily in data pipelines, evaluation sets, and continuous improvement loops that make your AI better over time.
AI systems need measurement to improve. We build evaluation frameworks alongside every system — not as an afterthought. If you can't measure accuracy, you can't improve it. We define what "good" looks like before we build.
Start with our AI Readiness Assessment — a structured evaluation of where AI can create real value in your business, delivered in two weeks.