AI Engineering
AI that works in production, not just in the demo
We treat AI features as software: evaluated before we build, grounded in your own data, and monitored after we ship, not judged on how good the demo looked.
Talk to Us About AIThis is probably you if
- Your team spends real hours every week on a task that's mostly pattern recognition: support triage, document review, categorization
- You tried an AI pilot that looked great in testing and fell apart the moment real users touched it
- You're sitting on valuable internal data that nobody's actually using to speed up decisions
- You want AI in your product, but not a feature that confidently makes things up in front of a customer
Why most AI projects stall
Building an AI demo is easy. Building an AI feature that holds up under real, messy, adversarial usage is not. Most AI projects that stall don't fail because of the model, they fail because nobody defined what "good enough to ship" actually meant before building started, so there was no way to know if it was ready.
What's included
- 01Problem scoping: an honest read on whether this is actually an AI problem, or a data or workflow problem wearing an AI costume
- 02Retrieval-augmented systems that ground answers in your own data, instead of a generic model guessing at your business
- 03AI agents that carry out multi-step workflows, not just single-turn responses
- 04An evaluation harness built before the feature, so "is this good enough to ship" has a real, tested answer
- 05Production deployment behind feature flags, with monitoring and a fallback path for when the model is wrong
- 06Integrations with leading AI providers such as OpenAI and Anthropic, and with the business platforms you already run on, from CRMs like Salesforce and HubSpot to internal tools and support systems
- 07Intelligent document processing that turns unstructured files (PDFs, scanned forms, emails) into structured, usable data
- 08Chatbots and conversational interfaces, built on the same grounding and evaluation approach as everything else here, not treated as a lesser afterthought feature
Our approach
- 01
Scope
Confirm this is genuinely an AI-shaped problem before writing any code.
- 02
Ground
Connect the feature to your actual data and systems, so it answers from what's true for your business.
- 03
Evaluate
Build a labeled evaluation set before the feature exists, so shipping is a judgment backed by evidence, not a guess.
- 04
Ship and monitor
Deploy behind a feature flag with a fallback path, then tune based on real usage, not just launch-day performance.
Sound like the help you need?
Talk to Us About AIIllustrative example
A mid-market logistics operation manually sorting hundreds of incoming vendor invoices a week can move to automatic extraction and routing, with low-confidence cases flagged for a human instead of guessed at, cutting a multi-day manual process down to hours.
What you walk away with
- 01An AI feature that's been tested against a real evaluation set, not just a demo script
- 02A monitored production deployment with a defined fallback when the model is uncertain
- 03Less manual time spent on the process the AI now handles
- 04A system that's honest about its own limits, instead of one that confidently gets things wrong
FAQ
Which AI providers do you work with?
We integrate with leading providers such as OpenAI and Anthropic, and choose based on what fits the specific task, cost profile, and data sensitivity of your project, not a single default.
How do you keep it from hallucinating in front of customers?
Grounding the feature in your actual data, building an evaluation set before launch, and keeping a human-in-the-loop fallback for low-confidence cases. None of that is optional in how we build these.
We already have an AI feature that isn't working well. Can you fix it instead of starting over?
Often, yes. A lot of AI engagements start as a diagnosis of an existing feature rather than a rebuild from scratch.
How long before we see something in production?
Typically three to eight weeks for a first version, depending on how ready your data is and how complex the integration is.