Starter Services · Generative AI Pilot
One GenAI use case. Running in your business. Measured before you scale it.
Bring us a business metric worth moving. We take one defined use case from workflow to a running GenAI system, then measure quality, adoption and unit economics so you can decide whether to scale it, change it or stop.
See the use casesSelected outcomes from engagements in the same problem families. They are not performance guarantees for this pilot.
Common starting points
Popular GenAI use cases, scoped down to something you can actually prove.
These are common places to start, not a menu you have to choose from. The pilot works best when the use case is tied to one business metric, one accountable owner and a bounded workflow.
Customer Service
A grounded service assistant that resolves routine questions and knows when to hand off.
Answers from your policies, product information and service history, with citations or source traceability where the workflow requires it. It can serve customers directly or support agents first, depending on risk and readiness.
Enterprise Knowledge
An internal knowledge assistant that finds the answer without making employees hunt for it.
Searches approved policies, procedures, manuals and internal content, respects access boundaries, and returns a direct answer with the source material behind it.
Sales
An RFP and proposal copilot that turns approved knowledge into a first draft your team can trust.
Pulls from approved case studies, capabilities, product facts and prior responses to draft answers, flag missing evidence and route claims through the review path before they leave the building.
Marketing
Content generation inside the brand and approval rules you already use.
Generates first drafts, variants, summaries or localizations from approved source material, then keeps the human review and compliance path intact rather than routing around it.
Finance & Legal
Document review and extraction that sends the uncertain cases to a person instead of pretending certainty.
Reads contracts, invoices, claims or other document streams, extracts or compares what matters, and uses confidence and rules to route exceptions into a human review queue.
Operations
An SOP and incident copilot that turns scattered operational knowledge into next-step guidance.
Grounded in manuals, work orders, incident histories and approved procedures, it helps a frontline team diagnose an issue, find the right procedure, draft the next action and escalate when the evidence is weak.
Have another use case in mind? Good. Bring us the metric, workflow and systems involved. We will tell you whether it fits this pilot or belongs somewhere else.
What makes it more than a demo
It has to survive your data, your controls, your users and your economics.
A polished demo can prove that a model is capable of the task. The pilot is designed to prove whether the system can perform the task in your operating environment, at a quality and cost you can defend.
Your data and permissions
Connected to the real or production-representative source set, with the gaps, formats, entitlements and edge cases that matter after launch.
A real evaluation harness
A test set built from your cases and scored against the thresholds that matter for the use case, not a single generic accuracy number.
Inside agreed controls
Identity, access, logging, human review and escalation are designed with the people who own security, risk and the workflow.
Unit economics measured
Cost is measured in the unit the business actually consumes: per conversation, document, workflow run, case, asset or other agreed unit.
Real or production-representative data, approved integrations, representative users, agreed security controls and expected workload. A customer-facing production launch is included only when it is approved and inside the written scope.
You leave with
The system and the evidence behind the next decision.
Not a slide deck explaining what might work. A bounded system, the mechanism used to judge it, and the operating numbers required to decide what happens next.
A running GenAI system
Built in the agreed environment, connected to the defined data and workflow boundary, and handed over with the engagement-specific source.
The evaluation harness
Your test set, scoring logic and acceptance thresholds for quality, policy behavior, latency and other use-case measures.
Measured unit economics
The cost of operating the system at expected volume, plus the architectural and workflow levers that move that cost.
A scale, revise or stop decision
The evidence, tradeoffs and recommendation presented live to the people who own the next investment decision.
How it runs
Four stages inside one fixed window.
The two-to-four-week clock starts at kickoff once the access, data, environment and participant prerequisites written into the proposal are available.
Bound
Fix the metric, workflow, data sources, integrations, user group, environment and acceptance thresholds in writing.
Build
Connect the data, build the system and create the evaluation harness alongside it rather than at the end.
Run
Put it through real or production-representative work. Measure quality, adoption, latency, exceptions and operating cost.
Decide
Present what moved, what did not, what it costs, and whether the evidence supports scaling, revision or stopping.
Fit
A pilot is useful only when there is a real decision waiting at the end.
Two short lists. If you recognise yourself in the first, the pilot is the right size. If the second reads truer, we will point you at the engagement that answers your actual next question.
This is a good fit when
- You can name the business metric or operating pain that matters.
- There is an accountable owner who can make a scale-or-stop decision.
- Representative data and subject-matter experts are available.
- The workflow can be bounded to a small number of systems and users.
- You need evidence before committing to a larger implementation.
Start somewhere else when
- You are still asking what AI might be useful for across the organization.
- The use case requires an enterprise-wide platform replacement before it can run.
- No one owns the process or the metric you want to improve.
- Representative data cannot yet be accessed or used.
- You need a strategy, roadmap or readiness decision before a build.
Where it sits
Assessment, pilot, scale. Use the smallest engagement that answers the next question.
Three engagements, one path. Most clients enter at the step that matches what they already know, and move right only when the evidence says to.
AI & Data Assessment Sprint
Find and score the use case, readiness gaps and dependencies before anyone starts building.
See the Assessment SprintGenerative AI Pilot
Build one bounded use case, measure it under production conditions and make the investment decision on evidence.
Start with the metricGenerative AI & AI Agents
Productionize, integrate and extend the system across additional workflows, teams or use cases.
See the practiceBefore you ask
The buying questions that matter.
The seven things procurement, security and the sponsor ask before a pilot is approved, answered the way we answer them in the proposal.
What counts as production conditions?
Your real or production-representative data, approved integrations, representative users, agreed security and access controls, and an expected workload. Customer-facing production deployment is included only when it is approved and inside the agreed scope.
When does the two-to-four-week window start?
At kickoff, once the access, data, environment and participant prerequisites written into the proposal are available. That keeps an access delay from becoming a fictional delivery delay.
What does it cost?
Pilots start at $30,000. The exact fee is fixed in writing before work starts and depends on the agreed workflow, systems, integrations, user group and environment. Work inside that boundary is not billed hourly.
Which models and platforms?
We are not tied to one model or cloud. Recommendations follow your security requirements, existing technology footprint, workload, quality targets and unit economics.
What if the pilot does not hit the success threshold?
That is a valid result. You still leave with the system, harness, findings and measured economics needed to stop, revise or redirect the use case rather than scale it on optimism.
Who owns the code and evaluation assets?
The engagement is designed for handoff. You receive the engagement-specific source and evaluation assets, subject to the licensing terms of any third-party components used. Ownership details are written into the agreement before kickoff.
Will our data be used to train public models?
The answer depends on the enterprise service and contract selected. Data handling, retention and training terms are documented before build, and the architecture is configured to follow your approved controls.
Start from the business
Tell us the number you need GenAI to move.
Do not start with a model name. Give us the function, metric, workflow and systems involved. We will tell you whether it fits the pilot and, if it does, define the boundary, window and fixed fee before you commit.
Still deciding what is worth building? Start with the AI & Data Assessment Sprint.
Have a use case? Start with the metric, workflow and systems involved.