01
New AI feature
The method starts with acceptance and product integration, not model selection.
Brainic method
From uncertainty to an operable release.
From technical fit and acceptance criteria to delivery, observability, and handover for AI workstreams inside existing products.
The stages are not fixed-duration packages. Each must remove a technical or operational uncertainty.
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01
Fit & constraints
Establish whether the problem fits Brainic before turning the conversation into a project.
- Identify users and the internal owner
- Clarify the data and systems involved
- Bound the outcome and identify non-fit conditions
Output
A fit decision and a clear workstream statement.
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02
Technical assessment & acceptance
Turn the ambition into a system that can be accepted or rejected against concrete criteria.
- Map architecture and data flows
- Define acceptable quality, cost, and latency
- Assess security and operational risk
Output
Accepted scope, evaluation criteria, and architecture decisions.
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03
Build & evaluate vertical slices
Ship complete paths through the system, not isolated components integrated only at the end.
- Integrate into the existing product and stack
- Run evaluations on relevant data
- Add guardrails, fallback paths, and observability
Output
A functional increment evaluated under production-like conditions.
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04
Launch, observe & hand over
Launch is controlled and final ownership can be taken over by the internal team.
- Define rollout and operational fallback
- Monitor quality, cost, and latency
- Document and transfer ownership
Output
Observable release, documentation, and explicit ownership.
Same method, different emphasis
The starting point changes the primary risk.
02
Pilot that needs hardening
The focus shifts to evaluations, guardrails, observability, and edge-case behavior.
03
Foundation blocking AI
APIs, data, and cloud enter scope only as far as the outcome requires.
Working principles
Less theater. More control.
- Bounded scope
- An acceptable outcome is more useful than an open backlog.
- Evaluation before optimism
- Quality is measured on cases that matter, not demonstrated in a demo.
- Integration into the real system
- The existing product, data, and operations are part of the problem.
- Explicit ownership
- At the end, it must be clear who operates, observes, and decides.
First step
Before estimating, we check whether the workstream fits.
Share the technical context and intended outcome. A perfect brief is not required.