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The Readiness Assessment

A 5-day engagement that maps your data, surfaces high-ROI AI candidates, and recommends a pilot — fixed price.

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Case study
Tier-1 bank cuts reconciliation 92%

Agentic reconciliation across 14 source systems — six-week pilot, full rollout in one quarter.

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New
Private AI on dedicated GPUs

Frontier-class models on isolated infrastructure — your data never leaves the perimeter.

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Latest
Field notes: agentic eval at production scale

How we ship and operate eval harnesses for systems running ten-million-plus actions a month.

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Founder
Rohit Wakode — Founder & Director

B.Tech IIT Bombay · LLB GLC Mumbai. Building intelligent enterprise systems in India since 2014.

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Service · 03

MLOps & Evaluation

Production observability, safety harness, drift detection, cost optimisation.

Shipping a model is the easy part; keeping it good in production is the job. We build the observability, evaluation, and drift detection that keep AI honest — and the cost controls that keep it affordable.

If you can't measure it, you can't trust it. We make production AI measurable.

FormatObservability, evals, drift, cost
TeamML platform / SRE
OutputAI you can trust in production
EntryScoped engagement
01 — What's included

Keep production AI honest.

/ 01

Production observability

Traces, latency, and quality dashboards for every model and agent.

/ 02

Continuous evaluation

Ongoing grading against ground truth, with regression gates.

/ 03

Drift detection

Catch data and quality drift before users do.

/ 04

Safety harness

Guardrails, validation, and incident playbooks.

/ 05

Cost optimisation

Right-sizing, caching, and routing to cut spend.

/ 06

CI/CD for models

Versioned, tested, reproducible deployments.

02 — How we engage

From first call to production.

01

Instrument

02

Gate

03

Monitor

04

Optimise

STEP 01

Instrument

We add tracing, evals, and dashboards across your AI systems.

STEP 02

Gate

Regression and quality gates stop bad versions reaching users.

STEP 03

Monitor

Drift and cost monitoring with alerting and playbooks.

STEP 04

Optimise

Ongoing cost and quality tuning.

03 — Where it pays

Use cases.

Production observabilityContinuous evaluationDrift detectionCost optimisationCI/CD for modelsIncident playbooks
04 — Engineering

Stack & standards.

Observability
OpenTelemetry
Eval dashboards
Tracing
Quality
Regression gates
Drift detection
Guardrails
Cost
Caching
Routing
Right-sizing
05 — Outcomes

What good looks like.

Trusted
In production
Quality you can see and prove.
Stable
No silent drift
Caught before users notice.
Cheaper
Optimised
Spend controlled, not discovered.
06 — Questions

Answers, before you ask.

Do you work with our existing models?
Yes — MLOps wraps your current models and agents with observability, evals, and cost controls, whoever built them.
How do you catch drift?
Continuous evaluation against ground truth plus data-drift monitoring, with alerts before quality visibly degrades.
Can you cut our AI costs?
Usually — caching, routing, and right-sizing typically reduce spend significantly without hurting quality.
Ready when you are

Let's talk about MLOps & Evaluation.

Start with a fixed-price 5-day Readiness Assessment or a 6-week pilot. Senior engineers, measurable evals, and a system you own on handover.

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