AI isn't a feature we bolt on. It's how we build.
Our engineers work daily with the frontier of applied AI — turning language models and machine learning into dependable, production-grade software, not demos.
Six ways we put AI to work.
Custom LLM Applications & Agents
Purpose-built assistants and autonomous agents wired directly into your workflows and data.
Retrieval & Knowledge Systems
RAG pipelines that ground AI output in your own documents, product data, and policies.
Computer Vision & Predictive Models
From image classification to forecasting — models trained, evaluated, and shipped responsibly.
MLOps & Scalable Infrastructure
Model serving, monitoring, and cost control so your AI systems hold up in production, not just in a demo.
Workflow & Process Automation
AI-driven automation that removes repetitive work from operations, support, and back-office teams.
Evaluation, Safety & Guardrails
Structured testing, prompt-injection defenses, and human-in-the-loop checkpoints before anything ships.
The RAG architecture we actually default to.
Most "we do RAG" pitches stop at "we use a vector database." Here's the level of detail that separates a retrieval system that works from one that quietly hallucinates.
Chunking & Embedding
Semantic chunking (not fixed character counts) so a chunk boundary doesn't split a sentence mid-thought — with overlap tuned per document type, since a legal contract and a Slack export need different strategies.
Hybrid Retrieval, Not Just Vector Search
Dense vector search paired with keyword (BM25) search and reciprocal rank fusion — pure embedding similarity misses exact matches like part numbers, error codes, and names that vector search alone handles poorly.
Reranking Before Generation
A cross-encoder reranker narrows the top-k retrieved chunks before they reach the model — cutting the "confidently wrong because of an irrelevant retrieved chunk" failure mode significantly.
Responsible AI, in practice — not just policy.
"Responsible AI" is often a slide in a deck. For us it's a set of concrete engineering practices we apply on every project, because unreliable AI is worse than no AI at all — it erodes trust the first time it confidently gets something wrong.
What that looks like in a project
- Defining what "correct" means for your use case before writing prompts, so success is measurable, not vibes-based
- Building an evaluation set from real (or realistic) examples, including edge cases and adversarial inputs
- Choosing the smallest, cheapest model that reliably meets the bar — not defaulting to the biggest one
- Designing graceful failure states: when the AI isn't confident, the system should say so or hand off to a human
- Monitoring in production for drift, cost, and failure patterns — not "ship and forget"
Where we start
Most engagements begin with a short discovery phase to identify one well-scoped, high-value use case — rather than an open-ended "add AI everywhere" mandate. A focused first win builds the internal confidence (and the evaluation infrastructure) to expand from there.
A practical path in, not a leap of faith.
AI Readiness Assessment
A short, focused review of your data, workflows, and systems to identify where AI can realistically create value.
Pilot Build
A scoped pilot on one real use case, with clear success criteria agreed upfront — proof before scale.
Production & Scale
Hardening the pilot into a monitored, production-grade system, then expanding to adjacent use cases.
What people ask before starting an AI project.
No. We handle model selection, evaluation, integration, and infrastructure end to end. You don't need in-house AI expertise to work with us — that's the point of hiring a specialist team.
Constantly. If a rule-based system, a simple lookup, or traditional software solves the problem more reliably and cheaply, we say so — even though it's a smaller engagement for us. Using AI where it doesn't earn its place erodes trust in the parts where it does.
A concrete set of test cases — including tricky and adversarial ones — that the system must handle correctly, checked automatically before any change ships. It's the difference between "it felt better" and "we can prove it's better."
We review each provider's data handling and retention terms against your requirements, use enterprise/API tiers with stricter data policies where warranted, and can architect around self-hosted or private-deployment models when needed.
It depends heavily on scope — a focused pilot is far cheaper than a full production system. We scope this honestly during the AI Readiness Assessment before any commitment.