AI workflow benchmarking
Turn a real business workflow into an eval suite: prompts, data samples, scoring rubrics, latency checks, and cost comparisons.
Consulting
Benchmark your AI workflow before deploying it.
Edxperimental Labs helps teams choose models, evaluate AI systems, and build confidence before committing to a provider, agent workflow, or production architecture.
Model and provider selection
Compare OpenAI, Anthropic, Google, open-weights, and inference providers against your accuracy, privacy, cost, and speed constraints.
Deployment review
Stress-test RAG systems, agent workflows, prompt pipelines, and internal AI tools before they become expensive production mistakes.
Engagement packages
Choose the smallest sprint that answers the buying question.
The consulting product is built around decision artifacts: a buyer should leave with evidence, a risk map, and a clear next action.
3-5 days
Diagnostic sprint
Teams that need to decide what to test before committing engineering time.
Workflow risk mapEvidence request listBenchmark scopeOwner handoff
1-2 weeks
Benchmark sprint
Teams comparing model/provider options for a concrete workflow.
Task packetsModel run tableTrace reviewDeployment recommendation
2-4 weeks
Deployment review
Teams preparing an agent, RAG system, or internal AI tool for production.
Failure auditCost/latency envelopeFallback planProduction risk memo
Sprint timeline
From vague AI idea to decision memo.
A good engagement compresses ambiguity quickly: define the work, score the alternatives, inspect the failures, and decide what to ship or avoid.
Day 0
Intake
Capture workflow, user journey, current stack, success metric, and decision deadline.
Day 1
Task design
Create task packets, expected outputs, scoring rubric, and evidence requirements.
Days 2-4
Runs
Compare model/provider candidates with trace capture, cost/latency logging, and reviewer notes.
Day 5+
Decision
Deliver recommendation, risk register, next tests, and production-readiness map.
What to send first
A good brief makes the first call useful.
Workflow
One real workflow with examples, owner, volume, and failure cost.
Data
Representative documents, prompts, tickets, screenshots, or redacted traces.
Constraints
Latency target, privacy boundary, budget, vendors under consideration, and compliance requirements.
Decision
The exact question the benchmark must answer: buy, build, switch, ship, pause, or redesign.
Owner routing
Research and benchmark design
Sanjay leads task design, scoring rubrics, benchmark interpretation, and technical delivery.
Discovery and solution mapping
Saujas leads sales engineering, client discovery, sprint scope, and solution fit.
Shared handoff
Both streams converge into a decision memo, benchmark report, and next-step implementation plan.
Client intake packet
Useful before the first consulting call.
These generated templates turn an interested lead into a benchmark-ready brief: workflow, data, constraints, candidate systems, acceptance evidence, and sprint scope.
MarkdownBenchmark intake templateA buyer-facing worksheet for workflow, data, constraints, candidate systems, and acceptance evidence.MarkdownConsulting sprint proposalA reusable proposal outline for diagnostic, benchmark, and deployment-review sprints.JSONIntake schemaStructured fields for turning the intake packet into a future form, CRM record, or Studio workflow.
Consulting Operating Plan
A handoff system for turning leads into benchmark sprints.
The operating plan separates sales-engineering discovery, technical scoping, sprint proposal, and delivery review so Sanjay and Saujas can move a buyer from vague AI interest to evidence-backed decision packet.
4
Handoff stages
5
Readiness gates
4
Delivery artifacts
Lead qualification
Saujas
Discovery note
Technical scoping
Sanjay Prasad
Benchmark scope
Sprint proposal
Saujas
Proposal memo
Delivery review
Sanjay Prasad and Saujas
Decision packet
Readiness gateProof required
Workflow specificityOne workflow with owner, inputs, outputs, volume, and failure cost.
Evidence accessRepresentative prompts, documents, tickets, traces, screenshots, or policies are available.
Decision deadlineThe buyer knows whether the sprint must answer buy, build, switch, ship, pause, or redesign.
Candidate systemsAt least two model/provider/agent routes and one baseline process are named.
Review ownerA human reviewer can judge correctness, partial credit, and unacceptable failures.
Delivery artifact
Workflow risk map
Shows where the current process fails, what AI could improve, and what should remain human-reviewed.
Delivery artifact
Benchmark task packet
Defines inputs, expected outputs, rubrics, holdouts, and evidence requirements before any model runs.
Delivery artifact
Run and trace ledger
Captures model ids, prompts, artifacts, latency, cost, reviewer notes, and failure classes.
Delivery artifact
Deployment decision memo
Turns evidence into a recommendation, fallback route, monitoring plan, and next-sprint backlog.
Consulting Service Catalog
A buyer-facing menu for picking the right sprint.
The generated catalog turns consulting into scannable services with owners, buyer questions, starting inputs, delivery artifacts, readiness scores, and the next action needed from the client.
5
Services
2
Owners
4
Artifacts
Sanjay Prasad
AI workflow benchmarking
Which model, provider, or agent route should handle this workflow?
Teams with one concrete process, examples, and a decision deadline.
Starting inputs
Workflow examplesExpected outputsCandidate systemsFailure cost
Delivery artifacts
Task packetRun tableTrace ledgerDecision memo
Send one workflow and two candidate routes for a diagnostic scope.
Sanjay Prasad
Agent reliability review
Can this agent complete work safely across tools, browser state, and handoff boundaries?
Teams piloting coding agents, browser agents, support agents, or internal automation.
Starting inputs
Agent traceTool permissionsSuccess criteriaHuman handoff rule
Delivery artifacts
Reliability scorecardFailure taxonomyTool-risk mapRelease gate
Share a current agent demo, transcript, or run log for trace review.
Sanjay Prasad
Model and provider selection
What should run on frontier APIs, faster hosted models, open-weight inference, or human review?
Teams balancing quality, latency, cost, data boundary, and vendor risk.
Starting inputs
Monthly volumeLatency targetPrivacy boundaryProvider shortlist
Delivery artifacts
Route matrixCost curveFallback policyProcurement memo
Send workload volume, context length, output length, and candidate vendors.
Sanjay Prasad and Saujas
AI security and risk sprint
Where can prompt injection, excessive agency, data exposure, or weak escalation break the workflow?
Teams moving from demo to production with tool access, customer data, or policy-sensitive outputs.
Starting inputs
Threat modelTool scopeSensitive fieldsIncident examples
Delivery artifacts
Security task packRisk registerControl deckLaunch blockers
Share the riskiest tool/action path and the data the agent should never reveal.
Saujas
Sales-engineering diagnostic
What is the smallest sprint that would answer the buyer's AI decision?
Founders or operators who need a scoped benchmark before a larger AI build.
Starting inputs
Business goalStakeholder mapCurrent workflowBudget signal
Delivery artifacts
Discovery memoSprint scopeAccess checklistProposal outline
Send a short buyer problem statement and target decision date.
Benchmark brief form
Submit a workflow for a first-pass consulting diagnosis.
This form writes a structured intake record for Sanjay and Saujas: workflow, decision, timeline, data boundary, and candidate systems. It is the working bridge from the public consulting page to a future CRM.
Discovery
Saujas routes the buyer context and scope.
Benchmark
Sanjay turns the brief into task packets and evidence gates.
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Team
Two-person core team, built for research and client delivery.
Sanjay leads benchmark and systems direction. Saujas is the sales engineer for discovery, scoping, and client-facing solution design.
Founder, AI benchmarking and systems
Sanjay Prasad
Research direction, benchmark design, model evaluation, and technical delivery.
sanjay@edxperimentallabs.comSales engineer and client solutions
Saujas
Client discovery, solution mapping, technical sales, and consulting coordination.
saujas@edxperimentallabs.com