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 governance evidence, uses Measure Loop to improve the system, 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 business workflow, two to four personas, one or two knowledge domains, and limited tool access.
Expansion shape
After evidence lands, add more teams, richer memory, additional tools, stronger policy profiles, and HA topology.
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, memory, retrieval, model route, policy, tools, 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
RBAC enforcement, model whitelist behavior, policy blocks, tool approvals, memory scope, and audit completeness.
Operator readiness
Install time, health checks, drift visibility, backup verification, rollback posture, and support bundle quality.
Usage signal
Heartbeat state, active users, inference calls, tool calls, last-seen recency, and cap posture.
Commercial fit
Named production owner, budget owner, urgency, expansion path, license size, and renewal readiness.
Measure Loop Evidence
The pilot should improve the intelligence layer as users work.
Srasta Measure Loop captures more than answer success. It connects persona, workflow, governed context, inference route, policy state, tool action, feedback, and correction patterns so the organization learns which prompts, memory scopes, routes, and playbooks should improve.
Before / after baseline
Current workflow effort and quality compared with measured Srasta-assisted output.
Run-level evidence
Context used, model route, latency, tool action, policy decision, audit record, and outcome label.
Improvement backlog
Prompt fixes, retrieval improvements, memory boundary changes, tool policy changes, and persona training.
Promotion recommendation
Promote when quality improves, governance invariants hold, token cost is acceptable, and recovery paths work.
Timeline
A strong pilot can move quickly without skipping evidence.
Qualification
Confirm sponsor, workflow, data boundary, infrastructure path, users, and success rubric.
Controlled install
Deploy Srasta, activate license, configure identity, model route, knowledge, memory, and policy.
Workflow runs
Users run real tasks; Measure Loop captures outcomes, corrections, policy behavior, and operator health.
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 personas, knowledge sources, tool policies, integrations, workflows, and reporting needs.
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, policy behavior, audit completeness, operator health, inference latency, and the improvement path for prompts, memory, tools, and routing.
What should the executive readout contain?
The readout should include the baseline, pilot scope, users and personas, workflow results, Measure Loop findings, security and audit evidence, operator findings, risks, ROI narrative, and the production expansion 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.