Good pilot signal
Recurring work, known users, measurable baseline, governed data, clear owner, and executive visibility.
Evaluation & Pilot Playbook
A Srasta pilot should prove value, control, and readiness.
The goal is not to install AI and hope usage proves the case. A strong pilot starts with a bounded workflow, measures before-and-after outcomes, captures the evidence available from the tested deployment, and ends with an executive decision on production expansion.
Pilot Operating Model
Run the pilot as a measurable operating loop.
Srasta pilots should connect the business workflow to the runtime evidence. The loop below keeps the pilot from becoming a demo: every phase creates evidence that can be reviewed by business, security, platform, and executive stakeholders.
Use-Case Selection
Pick a workflow that can prove business value and governance value.
Srasta is strongest when the pilot involves company-specific context, governed access, repeatable expert work, and a real decision loop. Avoid broad "AI assistant" pilots. Start with one workflow where better answers, faster review, fewer escalations, or clearer evidence matters.
Weak pilot signal
Vague experimentation, no baseline, no sponsor, no policy constraints, no path to budget, or no production owner.
Best initial scope
One workflow, a small user group, approved examples, one compatible client, and a review owner.
Expansion shape
Expand only after the evidence supports it. Check each additional model, client, integration, and deployment profile separately.
Stakeholders
Every pilot needs a clear owner for value, risk, and operations.
Executive sponsor
Defines the business decision, budget path, and production expansion threshold.
Workflow owner
Owns the baseline process, user adoption, output review, and final value judgment.
Security / risk
Reviews identity, data boundaries, audit evidence, model access, and tool policy.
Platform operator
Owns install, topology, runtime health, backup posture, upgrades, and support handoff.
Pilot users
Run real tasks, provide feedback, flag corrections, and expose workflow edge cases.
Srasta team
Guides deployment, measures outcomes, tunes the loop, and prepares the executive readout.
Success Metrics
Measure adoption, outcome quality, governance, and operating readiness together.
A pilot that only measures model output is incomplete. Srasta should prove that the enterprise can run AI inside real constraints: identity, retrieval, model route, applicable policy, audit, recovery, and user feedback.
Workflow value
Cycle time, manual effort reduced, throughput, review time, escalation rate, and rework avoided.
Inference quality
Groundedness, answer usefulness, repeatability, correction rate, accepted outputs, and failure patterns.
Governance behavior
Identity and role checks, denied-access cases, model access, data scope, and available audit evidence.
Operator readiness
Install time, health checks, drift visibility, backup verification, rollback posture, and support bundle quality.
Usage signal
Available deployment health, licensed usage, inference calls, last-seen recency, and cap posture, paired with a manual workflow review.
Commercial fit
Named production owner, budget owner, urgency, expansion path, license size, and renewal readiness.
Reviewable evidence
Record what the tested workflow actually did.
Keep a small evidence log: the input and expected result, the reviewed output, supporting sources or tests, the model and client configuration, latency, access checks, and operator observations. Use it to decide what to fix and whether the workflow is worth expanding.
Measure Loop is a separate roadmap concept, not a pilot feature available today.
Before / after baseline
Current workflow effort and quality compared with measured Srasta-assisted output.
Run-level evidence
Approved example, configuration, reviewed output, source or test result, latency, and access decision where applicable.
Improvement backlog
Prompt, retrieval, client, configuration, and operating changes the team can retest.
Go/no-go recommendation
Proceed only when quality, access boundaries, cost, and operating responsibilities meet the agreed threshold.
Timeline
A strong pilot can move quickly without skipping evidence.
Qualification
Confirm sponsor, workflow, data boundary, infrastructure path, users, and success rubric.
Controlled install
Confirm a supported deployment, activate the license, and configure identity, model route, and the approved workflow.
Workflow runs
Users run representative tasks; reviewers record outcomes, access behavior, limitations, and operator observations.
Readout
Review evidence pack, ROI narrative, risks, expansion design, commercial fit, and production recommendation.
Executive Readout
The pilot ends with a decision, not a loose status update.
Business outcome
Baseline vs observed value, user adoption, workflow impact, and qualitative feedback.
Governance outcome
Identity, RBAC, policy, audit, data boundaries, and security-review findings.
Operating outcome
Topology, health, runtime truthfulness, backup readiness, rollback readiness, and support posture.
ROI narrative
Time saved, risk reduced, quality improved, rework avoided, and the cost of not operationalizing the workflow.
Production recommendation
Promote, iterate, pause, or expand, with the next deployment topology and license shape.
Expansion backlog
Additional roles, knowledge sources, integrations, workflows, and reporting needs; proposed tool policies require separate product scoping.
FAQ
Pilot playbook questions
When should a team run a Srasta pilot instead of only using the free trial?
Use the free trial for self-serve evaluation. Move to a pilot when the team has a concrete workflow, an executive sponsor, regulated or security-sensitive data boundaries, and a need to prove governance, operator visibility, and measurable workflow value.
What makes a good Srasta pilot use case?
A good pilot use case has a real user group, repeatable work, company-specific context, measurable baseline pain, clear policy constraints, and an outcome that an executive can value.
What should be measured during the pilot?
Measure workflow quality, answer grounding, repeatability, time saved, user adoption, escalation rate, access and policy behavior, operator health, and inference latency. Choose measures the current deployment can actually produce.
What should the executive readout contain?
The readout should include the baseline, pilot scope, users and roles, reviewed workflow results, security and audit evidence available from the tested deployment, operator findings, risks, costs, and a go/no-go recommendation.
Next Step
Start with a workflow that matters.
The best pilot is specific enough to measure and important enough to fund when the evidence is clear.