The friction
Product teams know they need AI capabilities, but building RAG systems, evaluations and safe deployment in-house means senior hires, long timelines and expensive mistakes with data privacy.
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Product
Product & engineering
Your product needs AI features, but building the capability in-house can slow delivery.
Pilot targetFaster path from prototype to production
Project startDate agreed in scope
ValidationMeasured against baseline
How the project runs
What the pilot should prove
What changes
From AI idea to a measurable product capability.
Pilot targetFaster path from prototype to production
The operating change
Prototype an AI capability around a defined user task, approved data and permission boundaries. Evaluate retrieval, output quality, latency and operating costs before release; engineers own code review, deployment and monitoring.
Best fit
SaaS companies and product teams.
Decision snapshot
Start with one user task and a representative evaluation set.
Start with one workflow, measure it against the current baseline and expand only after the result is visible.
Best first pilotOne user-facing AI capability with an evaluation set.
What we measureTask success, groundedness, latency, cost and user adoption.
Human controlFallbacks, permissions, review paths and monitoring are explicit.
Scale ruleKeep, improve or stop the pilot based on evidence before expanding.
Smart Product & engineering roadmap
From one AI use case to an evaluated, monitored capability
Move beyond demos by proving quality and fit before the capability reaches real users.
What happens today
Product teams know they need AI capabilities, but building RAG systems, evaluations and safe deployment in-house means senior hires, long timelines and expensive mistakes with data privacy.
Product teams know they need AI capabilities, but building RAG systems, evaluations and safe deployment in-house means senior hires, long timelines and expensive mistakes with data privacy.
Roadmap
See the operating journey stage by stage.
Open each stage to see the problem, action, deliverable and KPI behind the workflow.
Product & engineering AI pilot roadmap
Stage 1 of 7
Use case & success criteria
Clear go / no-go criteria
The problem at this stage
AI work often starts with a broad request to “add AI” without a bounded user task, baseline or acceptance bar.
Exactly what we do
Choose one user task, define who uses it, what inputs and outputs are allowed, where the current workflow fails and what result would justify continuing.
What you receive
Pilot brief, current baseline, task boundaries, acceptance criteria and a named owner.
Opportunity map
Eight practical areas we can improve
See discovery, knowledge, prototyping, evaluation and release controls as one product-engineering workflow instead of disconnected model experiments.
1. Use case & success criteria
Choose one user task, define who uses it, what inputs and outputs are allowed, where the current workflow fails and what result would justify continuing.
2. Evaluation set
Build a representative evaluation set with normal cases, edge cases, known failure cases and examples that must be handed to a person.
3. Knowledge, data & permissions
Identify approved sources, retrieval needs, system permissions, sensitive data boundaries and any tools or APIs the pilot is allowed to use.
4. Prototype & integration
Build the smallest end-to-end prototype, connect only the systems the pilot needs and add logging, fallback behaviour and visible source context where appropriate.
5. Evaluation & red-team
Run the evaluation set, review incorrect or unsupported outputs, test prompt-injection and misuse cases where relevant, and compare quality, latency and cost with the agreed thresholds.
6. Human review & release controls
Define review points, permissions, escalation rules, audit logging and rollback conditions before any broader release.
7. Monitored pilot & scale decision
Run the pilot with a limited audience, monitor quality and exceptions, compare the agreed KPIs with the baseline and document the scale decision.
8. Continuous improvement
Bring evaluation results, failure modes, latency, cost and user feedback into one recurring product review so release decisions are evidence-based.
Choose your first pilotRecommended starting point
One user-facing AI capability with an evaluation set.
Start with one product use case and a representative evaluation set, connect only the required knowledge or APIs and compare quality, latency and failure modes against the baseline.
What we build
- Technical discovery and feasibility check
- Architecture with privacy and cost constraints defined
- Focused prototype for real user testing
- Production build with evaluation and monitoring
How we measure it
- Task success
- Groundedness
- Latency
- Cost
- User adoption
AI in action
See how one AI capability moves from prototype to evaluated release
Why this approach works for Product & engineering
Start small, then connect
Choose one product capability and one evaluation target first. Expand only after product and engineering accept the quality, latency, cost and failure evidence.
Keep people in control
Fallbacks, permissions, review paths and monitoring are explicit.
Use the tools already in place
Connect the approved knowledge sources, APIs, repositories and observability tools the product already uses instead of creating an isolated AI layer.
Measure business outcomes
Review the agreed measures: Task success, groundedness, latency, cost and user adoption. Use these to assess business outcomes and decide the next step.
Product & engineering project process
1. DiscoveryInterview the owner, process lead and people who do the work today.
2. BaselineMeasure time, volume, quality, cost, delays or missed opportunities.
3. Build the pilotConfigure one focused workflow and connect only the data required for the first result.
4. MeasureCompare the pilot with the baseline and review adoption, quality and risk.
5. Decide and scaleKeep, improve or stop the pilot based on evidence, then connect the next stage.
6. Improve regularlyTurn the connected reporting into a recurring operating rhythm for the team.
Suggested start: one workflow can go live first; the next measurable improvement is added only after the first review.
Information we need
- Current workflow, owner and handoffs
- Three to six months of relevant examples, records or messages
- The systems, files, channels and approvals involved
- Current baseline for Task success, groundedness, latency, cost and user adoption.
- The person responsible for exceptions and final decisions
Initial diagnostic questions
- What is the biggest product & engineering bottleneck today?
- How often did it happen last month?
- What do manual work, poor answer quality, latency, support burden or failed user tasks cost today?
- Which system or data already records the problem?
- Can the first pilot run on one feature, user task, evaluation set or bounded product workflow?
Not sure which one fits you?
Book a free 30-minute discovery call. We will identify which use case is worth testing first and what evidence should decide the next step.
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