AI Consulting
For teams that have run pilots and want production systems. Most companies have a long list of AI ideas and zero AI in production. We help you pick the use cases that move a metric you care about, build the systems end-to-end & instrument them so you can tell whether they're working. The deliverable is a production system, not a slide deck.
Where we plug in
You've experimented with ChatGPT or Claude internally but nothing has made it into your product or workflows. Or you shipped an AI feature and the impact metrics look mushy. Or you need an internal AI tool that can read your data, docs & operational systems without leaking anything outside the org.
What we ship
- AI opportunity audits with prioritization
- RAG systems over internal knowledge bases
- LLM-powered reporting & metric APIs
- Agentic workflows & automation
- Internal AI tools for ops, sales, support
- Evals, observability & cost controls
- AI-augmented analytics & insight generation
- Model selection & integration strategy
A recent example
For an EdTech client we're building a reporting API that delivers near-real-time sales and customer metrics, alongside AI-driven analysis of employee performance. Phase one shipped in weeks and is already changing how the leadership team runs its weekly business reviews.
Typical engagement
4–16 weeks depending on scope. We always start with an audit plus prototype before committing to a full build.
Common questions
We've run ChatGPT pilots internally. Why hasn't anything reached production?
The usual gaps: no evals to prove the system works, no access to the internal data that makes it useful, no owner once the demo is over, and no cost or latency budget. We build those pieces alongside the AI itself (i.e., the parts that separate a demo from a system).
How do you measure whether an AI system is actually working?
Evals and instrumentation are part of every build: task-level accuracy checks, cost and latency tracking, and a metric tied to the business outcome the system was supposed to move. If we can't define that metric with you up front, we'll say the use case isn't ready.
Can an internal AI tool read our data without leaking it outside the org?
Yes. We build RAG systems and internal tools that run against your warehouse, docs & operational systems with your access controls, using API providers under zero-retention terms or models in your own cloud when that's the requirement.
What does an AI engagement cost and how does it start?
Every engagement starts with an audit plus prototype (typically 2–4 weeks) before we commit to a full build, so you're never signing a big number based on slides. Full builds run 4–16 weeks depending on scope; most land in the same $25K–$200K range as our other practices.
Often paired with
Data Modernization
For teams on legacy systems that can't keep up.
Learn more →Data Transformation
For teams whose data is messy, stale, or untrusted.
Learn more →Analytics & BI
For teams whose dashboards exist but nobody opens them.
Learn more →Data Strategy
For teams about to make a large data investment.
Learn more →Working on something like this?
Tell us what's going on in a few sentences. A partner (not a salesperson) reads it and replies within one business day with a concrete next step, whether or not we're the right fit.