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.
Six focused AI practices — each delivered by senior engineers who've shipped production systems.
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.
Real systems, running in production, solving problems that were costing our clients real money.
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.
Four principles that separate AI that ships from AI that sits in a slide deck.
Good AI candidates share a few traits: repetitive knowledge work, large volumes of unstructured data, or decisions that follow identifiable patterns. If your team spends hours on document review, data extraction, classification, or answering similar questions — AI can likely help. Our AI Readiness Assessment is designed to answer this question in two weeks.
An AI Readiness Assessment takes 2 weeks. A production MVP for a well-scoped use case (e.g., document extraction, support automation) typically takes 6–10 weeks. More complex systems with multiple data sources, custom fine-tuning, or deep integrations run 12–16 weeks.
Not necessarily. We can deploy models on your own infrastructure using AWS Bedrock, self-hosted open-source models, or private API endpoints. For regulated industries, we design architectures where sensitive data never leaves your environment. We'll recommend the approach that balances cost, performance, and compliance for your situation.
The AI Readiness Assessment starts at $5K. Production AI implementations typically range from $25K–$100K depending on scope. Ongoing API costs (LLM inference, vector DB hosting) usually run $500–$5K/month depending on volume. We provide transparent cost projections before every engagement.
We define success metrics and accuracy thresholds before building. Every system includes an evaluation framework that measures performance against human baselines. If accuracy isn't meeting targets, we iterate on the retrieval strategy, prompts, or data pipeline — not just hope it improves. We also build human-in-the-loop fallbacks for edge cases.
Start with our AI Readiness Assessment — a structured evaluation of where AI can create real value in your business, delivered in two weeks.