AI Implementation Consulting
Most companies don't struggle to come up with AI ideas — they struggle to get one into production and keep it running. We take a single high-value workflow from assessment to a working system, then host and operate it so your team uses the tool instead of maintaining it.
Why AI projects stall
A demo that works in a meeting is not the same as a system your team can rely on Monday morning. Pilots get stuck because no one owns the unglamorous parts: connecting to the real data, proving the answers are right, handling the edge cases, and keeping it healthy after launch.
The result is a backlog of promising prototypes and a team that now has to operate software it didn't plan to operate. We close that gap. We scope one workflow, build the production version with the evidence trail it needs, and then run it as an ongoing service.
How an engagement works
Three stages, each with clear deliverables. Every decision is small and reversible — you commit to one stage at a time.
Discovery
2–3 weeks | Fixed fee
We review the workflow, the data, and the use case, then give you a straight recommendation — build, buy, or wait — with an ROI case and a scoped pilot.
Build
4–8 weeks | Fixed scope
One workflow, one data source, success criteria set upfront. You get a production system with citations, evaluation, and documentation — not a prototype.
Managed Operations
Ongoing monthly
We host it, monitor it, and improve it while your team uses it. Teams with their own AI operations capability can take a full handoff instead.
See the full engagement model on the services page.
Two kinds of problems we implement
Both share the same backbone: answers that show their evidence and a system built to be audited, not just demoed.
Document intelligence
Answers pulled from your documents with a citation behind every claim — policies, regulations, manuals, contracts. Useful when the knowledge exists but is slow to find and risky to get wrong.
Cited document assistants →Operational data intelligence
Diagnostics and monitoring over equipment telemetry, event logs, and operational records, with the evidence for each finding. Useful when failures are expensive and the data is scattered across systems.
Operational data intelligence →Systems you can try right now
These are working demos, not screenshots. Each one is the kind of system we'd build and run for you.
USGA Rules Expert
Ask a golf-rules question and get an answer cited to the exact rule. A cited document assistant over a single authoritative corpus.
Compliance Copilot
Ingests policies and procedures, then triages and answers compliance questions with sources.
Pharma Intelligence
Analyzes FDA warning letters and regulated documents, surfacing risk signals and trends.
Furuno Diagnostics
A diagnostic copilot over marine-equipment telemetry that ranks likely faults and shows its evidence.
Frequently asked questions
What does an AI implementation consultant do?+
An AI implementation consultant takes a business workflow from idea to a working production system: assessing whether AI is the right fit, designing the architecture, building it against your real data, and proving the output is accurate. We also operate the system after launch, so the value continues without your team having to run AI software.
How long does it take to implement an AI system?+
Discovery takes 2–3 weeks and ends with a go/no-go recommendation. A focused first build typically takes 4–8 weeks for one workflow with one data source. We deliberately scope narrow so you see a working system in weeks, not a multi-quarter program with nothing to show.
Should we build a custom AI system or buy an off-the-shelf tool?+
Often you should buy. Discovery includes a build-versus-buy analysis, and if an existing product fits your workflow we will tell you. Custom build makes sense when your knowledge, data, or process is specific enough that a generic tool would either miss the nuance or expose you to unverifiable answers.
What happens after the system is built?+
By default we run it — hosting, monitoring, incident response, model and prompt maintenance, and quarterly reviews — as a monthly managed service. Your team uses the tool; we keep it healthy. If you have in-house AI operations, we can hand the system over with documentation and training instead.
How do you make sure the AI's answers are accurate and trustworthy?+
Every answer traces back to a source — a document section, an event-log entry, or a historical case — and that evidence requirement is built into the architecture rather than added on. We also ship an evaluation suite that tests the system against a known-good dataset so quality regressions are caught before users see them.
What kinds of companies do you work with?+
Teams with high-value knowledge trapped in documents, data systems, or manual workflows — pharma and biotech, manufacturing, maritime, legal and compliance, healthcare, and field service. The common thread is that getting an answer wrong is costly, so the answer has to be verifiable.
Have a workflow in mind?
Tell us what's taking too long or where your team is stuck. We'll give you an honest take — build, buy, or do nothing.
Describe your challenge