Services / AI Consulting & Development
We help you find the AI opportunities worth pursuing, prove them as prototypes, and ship them as production features your team can trust. Human-led, measured, and connected to the systems you already run.
Goals first
We agree what AI should accomplish and how success is measured before we build
Real prototypes
Working proofs of concept on your data, not slide decks
Production-ready
Security, permissions, and oversight designed in from the start
Sound familiar?
Every company is being asked about AI. If one of these sounds like yours, here’s where we’d start.
From the board
“What is our AI strategy?”
Where we’d start
AI strategy
We map where AI fits your goals and data, so you can answer with a plan instead of a vendor’s roadmap.
From product
“The pilot impressed everyone. It never made it to production.”
Where we’d start
Prototypes to production
We rebuild the pilot on real data with the people who will use it, then ship it into daily work.
From operations
“Half the team is already pasting customer data into public chatbots.”
Where we’d start
Human-led AI
We give the team private, approved tools with the controls your data needs. The use case stays. The risk goes.
From a team lead
“My best people spend hours a day reading and sorting documents.”
Where we’d start
LLM features
We automate the reading and sorting with language models, then measure the hours your team gets back.
Hearing something else? Start with a conversation →
What we build
We embed AI directly into your products and workflows. These are the capabilities we most often build.
Conversational interfaces, summarization, classification, and recommendation built on your business logic.
Train and tune models on your unique data and rules so the results fit your goals, not the average.
Search that understands meaning and context, across the documents, records, and knowledge your team already has.
Spot what is off, predict what is coming, and act before it becomes a problem.
Actions, alerts, and processes that fire from live behavior and conditions.
Where most work starts
Identify where AI would help, what it would cost, and what it would need, with a plain-language recommendation.
The uses worth pursuing, and the ones that are not, with the reasons.
Cost, data, and effort for each, in plain language.
One recommended starting point, scoped tightly enough to prove quickly.
What changes
Before A different answer from every vendor
After A clear map of what to pursue and what to skip, with the reasons
Before Hours of reading, summarizing, and routing
After Minutes, with people reviewing what matters
Before Tools in use without guardrails
After Permissions, approval gates, and oversight built into the system
Proof
Governance Compliance Tool
A text-enabled AI assistant built on a large language model trained for compliance. Employees report by chat or text; the assistant captures nine required fields and writes them to existing systems in real time.
8 min → 60 sec
Per report, across 100,000+ employees
How it runs
People decide at every stage. AI does the work it is good at, inside limits your team sets.
We start with your goals and your data, not a model. Where would AI change an outcome, and how would you know it did?
A working proof of concept, validated with the people who will use it.
The production version, with permissions, approval gates, and oversight in place.
Monitored, tested, and improving as models, systems, and your business change.
When models, systems, or your needs change, Managed AI Ops brings the work back to prototyping. Nothing is left to drift.
What to expect
AI work should be scoped tighter than most, because the risks of drift are real. Every engagement starts with an agreed goal and a way to measure it.
Book an assessmentQuestions we get
Less than most people think. Many valuable uses run on documents and records you already have. The assessment tells you what you have, what you would need, and whether the gap is worth closing.
People stay in the loop. Permissions, approval gates, and oversight are built into the system, so AI does its work inside limits your team sets.
We choose the model and vendor for each use case, starting from your goals and your data rather than from a model.
Managed AI Ops brings the work back to prototyping, so a change is tested before it reaches production. Nothing is left to drift.
YOUR NEXT DECISION
Bring the problem, the pilot that stalled, or the question the board asked. We will give you a grounded answer.
Come with a problem. You don't need a specification.
The first conversation helps define the next step, whether or not that step is with us.