User & feature
Name the user, the product action and the current alternative.
Awakast · AI engineering
AI discovery sprint for B2B SaaS
For B2B SaaS founders, product leaders and CTOs deciding whether to fund an AI feature, our sprint assesses the data and tests the assumption most likely to change that decision.
Plan an AI discovery sprintDefine the scope
Choose one feature decision
Investment decision
Ask what must be true to justify funding an integration—not whether a demo can look convincing.
Success & stop criteria
Agree success and stop criteria with the sponsor, including the quality a reviewer needs and the data or access gaps that would prevent a useful test.
What you can commission
Agree the feature decision, experiment boundaries and review participants before starting. The sprint produces evidence for a decision; a production build, launch and ongoing operation are separate scopes.
A feature decision brief
Identify the SaaS user, proposed product action and current alternative. Document what the sponsor needs to know before committing funds, including success criteria and reasons to defer or stop.
Data and experiment evidence
Assess representative data, permissions, integration constraints and the riskiest technical assumption. Include a bounded experiment or prototype only where it changes the decision, with results and limitations recorded.
A recommendation your team can use
Your written recommendation sets out the technical options, unresolved dependencies, cost drivers and prioritised next step. If a build is justified, outline a first integration and the acceptance questions still to settle with the delivery team.
A concrete starting point
Should your SaaS help customers diagnose a failed integration?
1
Hypothetical feature decision: test whether users can diagnose integration failures from their logs and product configuration, rather than only search the documentation.
2
Using a shareable sample of past incidents, compare a single-model baseline with a two-step flow: identify the likely cause from logs and configuration, then check each proposed fix against that evidence.
3
For each case, assess whether the flow identifies the known cause, points to relevant evidence and agrees with the recorded resolution.
4
Test a second model only if the first flow misses criteria agreed before the sprint; keep it only if better verification justifies its added latency and cost.
5
The product owner reviews cases where evidence conflicts.
6
If results remain weak, offer an evidence-linked incident summary or do not build.
7
The experiment uses only the shareable sample: it has no access to live customer accounts and changes no customer records.
Scope & acceptance
Close with an investment recommendation
- The decision package covers the scoped problem, data readiness, experiment evidence, technical options, assumptions, cost drivers and delivery risks.
- The next path may be an integration brief, a reshaped feature or a no-build recommendation.
- Discovery is not a guarantee of a production-ready specification; build scope and acceptance still need agreement before implementation.
How we approach the work
Frame with product and engineering
Bring the sponsor, product owner and someone who understands the workflow. Compare the proposed AI feature with the current experience and relevant existing tools before deciding what to test.
Test the integration diagnosis
For the integration-diagnosis feature, test whether a shareable sample of past incidents supports identifying known causes, citing relevant logs and configuration, and matching recorded resolutions; agree those checks before the sprint.
Review the evidence together
Present the test results, data gaps and architecture options to the decision owner. Agree whether to integrate, reshape the feature, resolve an access dependency or stop. A no-build recommendation is a valid outcome.
Controls, evaluation & ownership
Representative evaluation
Include difficult cases and failures, not only demonstration examples. The product owner agrees what useful output means; engineering records the assumptions and limits of the sample.
Safe experiment boundaries
Agree permitted data and processing before testing. Use only the shareable sample: the experiment has no access to live customer accounts and makes no changes to live customer records. A test result does not authorise a production release.
Decision and handoff ownership
The sponsor owns the investment decision. Name the product and technical owners for any later build; record which risks remain open and who must resolve them before implementation.
Before we start
Prepare access before the sprint
Bring the product owner, sample data you may share, system and permission constraints, and access to someone who understands the workflow.
Identify who can settle scope and who would own a later integration.
If the required data is unavailable, record the access dependency and decide whether to resolve it before running the experiment.
When to choose another path
- An open-ended request to explore AI without a user problem, decision owner or path to a product.
- A feature whose scope, data and acceptance criteria are already understood: discuss delivery rather than buying another analysis.
- A request to certify regulatory compliance or turn a prototype into a guaranteed production-ready specification.
Questions before you commission
Do we need a detailed specification first?
No. Bring the decision, user workflow, available data and known constraints. The sprint helps expose missing information; it should not require you to settle the architecture before the investigation.
Will we receive a prototype?
Only when it reduces a relevant uncertainty. A source assessment or integration test may be more useful. Agree the experiment deliverable before starting and treat prototype code as experimental until separately reviewed for production.
Can our team or another supplier use the findings?
Scope a written decision package with assumptions, test evidence, technical options and open questions. It can inform another team’s estimate. It does not replace their own design review or agreement on build acceptance.
How are duration and price agreed?
They depend on the decision, data access, stakeholders and experiment depth. Agree the timebox, commercial terms and deliverables after reviewing the brief; do not assume an open-ended research engagement.
Explore AI features delivered in real products
Use our engineering cases to frame the data and integration questions your feature decision needs to resolve.
Bring your AI feature investment decision
Send the feature idea and the decision you need to make. We will propose a timebox, deliverables and a final review with the decision owner.