Consulting

The AI journey from concept to production is rarely a straight line.

Edxperimental Labs helps teams scope, benchmark, engineer, deploy, and operate AI systems that can survive real enterprise constraints.

Talk to an expert Explore services

20+

projects delivered

Across AI apps, mobility, portals, analytics, and production automation.

60%

cost reduction target

Through prompt discipline, routing, pruning, and right-sized infrastructure.

Sub-2s

latency programs

P95 performance targets using caching, batching, streaming, and hot-path tuning.

Marquee client experience

Electric

Technologies

Anoma

Legal

Limited

National Instruments

Ecosystem recognition

Ventures

OpenAI

What we have already done.

Re-engineered legacy systems into state-of-the-art product architectures.

Conceptualized multiple SaaS-based solutions for SME and enterprise workflows.

Designed and developed hosted SME solutions focused on practical adoption.

Built a video streaming platform for a North American production house.

Equipped products with image analytics, recommendation engines, and chatbot capabilities.

Consolidated enterprise mobility capabilities across product and field workflows.

Built fast-emerging AI and ML expertise through production-oriented client projects.

AI production gap

We know where the obstacles are and how to navigate them.

The work spans performance, cost, integration, model lifecycle, monitoring, and function-specific business impact.

Fixed

Performance and scalability

Sub-2s P95 latency at scale

Caching and batching
Streaming and chunking
Parallelism
Hot-path optimization

Fixed

Cost optimization

Up to 60% cost reduction

Prompt and token discipline
Eval-driven pruning
Smart routing
Right-size infrastructure

Fixed

Enterprise integration

Secure, compliant workflows

APIs and webhooks
SSO and RBAC
ERP and CRM integration
Data governance

Fixed

MLOps and model management

Consistent, auditable releases

CI/CD for models
Lifecycle and versions
Evals and guardrails
Feature stores

Fixed

Reliability and monitoring

99.9%+ uptime targets

Tracing and logs
SLOs and alerts
Fallbacks and retries
Chaos testing

Fixed

Function-specific AI

Measurable business impact

Assistants for operations
RAG for knowledge
Agentic workflows
Human-in-the-loop

Consulting services

Start a brief

AI solutions and models

Design AI systems that perform beyond pilots: grounded RAG, assistants, classification engines, recommendation systems, image analytics, and evaluation loops.

Automation

Automate messy processes with systems that can handle changing inputs, approvals, human review, and enterprise constraints.

MLOps and deployment

Make models reliable, scalable, and production-ready with clean pipelines, observability, automated deployment, and rollback paths.

Application and product engineering

Modernize applications, migrate platforms, build SaaS products, and integrate secure services across web, mobile, and cloud.

Portal and e-commerce development

Build tailored portals and digital commerce systems with brand-specific UX, secure payments, analytics, and mobile-first architecture.

Workflow benchmarking

Compare models, tools, latency, cost, and accuracy against your real workflow before production spend locks in.

MLOps and deployment

Make your models reliable, scalable, and production-ready.

Most teams can train a model. The real challenge is production reliability: clean pipelines, observability, automated deployments, and zero surprises.

The problem most companies run into

Manual and inconsistent training workflows
No versioning or reproducible pipelines
Model drift going unnoticed
High compute cost
Fragmented monitoring and logs
Models behaving differently in staging and production

Assess the current ML lifecycle

Evaluate data flows, model workflows, tooling gaps, production constraints, and the operational risks that matter first.

Design standardized ML pipelines

Define validation, feature tracking, lineage, training pipelines, CI/CD, registry, version control, and staged promotion.

Automate training, testing, and deployment

Move model releases through traceable CI/CD instead of manual handoffs, with controlled promotion paths and auditability.

Build monitoring and observability

Implement dashboards and alerts for drift, latency, prediction quality, feature anomalies, infrastructure costs, and incidents.

Keep production stable

Optimize performance, reliability, rollback safety, cost, and cross-environment consistency after models go live.

Application, product, portal, and commerce engineering.

We modernize legacy systems, migrate applications, build SaaS products, integrate data and service layers, and create tailored portals or e-commerce platforms that match business workflows.

Application migrationRe-engineeringLegacy integrationSecure paymentsMobile-centric architectureAnalytics and CRM data capture

Proven results across industries

Healthcare

Clinical and operational AI workflows

Pharma

Knowledge, analytics, and automation systems

Medical conferences

Live AI event co-host and support systems

Electoral data

70 years of election data made queryable

95%

reduction in data query time

80%

decrease in technical barriers

40%

faster research workflows

Technical stack

Built with production tooling.

Pipelines and workflow

MLflow, Kubeflow, Airflow, Prefect

Deployment and serving

SageMaker, Vertex AI, KServe, Docker, Kubernetes

Versioning and registry

MLflow Registry, Feast, DVC

CI/CD

GitHub Actions, GitLab CI, Argo CD, Jenkins

Monitoring

Prometheus, Grafana, EvidentlyAI, OpenTelemetry

Cloud

AWS, GCP, Azure

Relevant case studies

View all

National Instruments Leadership Forum

AI Event Co-host System

A live AI voice assistant built in four days to co-host an enterprise leadership forum with scripted segments, listening mode, waveform monitoring, and guarded responses.

US-based stealth startup

On-device AI Classification System

A hybrid classification architecture combining lightweight on-device inference with server-side LLM routing for a large category taxonomy.

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.

Handoff stages

Readiness gates

Delivery artifacts

Operating plan Consulting manifest

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.

Services

Owners

Artifacts

Service catalog Catalog JSON

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.

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.com

Sales engineer and client solutions

Saujas

Client discovery, solution mapping, technical sales, and consulting coordination.

saujas@edxperimentallabs.com

Next step

Benchmark the workflow before production spend becomes irreversible.

Talk to an expert