For fractional CTOs, CDOs, and data advisors

Keep roadmap authority. Add technical reality checks.

You own the executive relationship, roadmap, and prioritization. We inspect the data and systems underneath the plan, then turn uncertainty into a scored first-win scope.

The Partner-Branded AI/Data Readiness Sprint is for advisors who know the client needs sharper technical evidence before making a hiring, budget, or roadmap commitment. MLDeep supplies the hands-on inspection layer while you keep the strategic seat.

$10K-$15K fixed · 2 weeks Entry offer: AI/Data Readiness Sprint Best for: executive roadmaps, AI initiatives, data operating model decisions Output: system map, feasibility score, first-win scope

20-min call · bring one executive roadmap question · leave with a written feasibility read · no slide deck.

Snowflake BigQuery Redshift dbt Airflow Terraform Looker Sigma Salesforce HubSpot Claude / OpenAI APIs dbt Labs Registered Partner
Anmol Parimoo
Anmol Parimoo, founder · MLDeep Systems 9 years in data engineering, cloud infrastructure, and AI · dbt Labs Registered Partner · delivery partner with Domain Methods · published researcher (AgentDoctor)
Full background →
Role split

You hold the strategy. MLDeep validates the technical path.

What you keep

  • Executive relationship and advisory authority
  • Roadmap framing and investment sequencing
  • Org design, hiring, governance, and operating model judgment
  • Final recommendation to the client leadership team

What MLDeep handles

  • Technical reality check against current systems and data
  • Data/system map across warehouse, CRM, BI, APIs, and automation paths
  • Feasibility scoring for AI, analytics, and automation opportunities
  • First-win scope that can be funded before the full roadmap
Why this holds up

Senior delivery you can verify, not a pitch.

You hold the roadmap and the board relationship. We are the build arm behind it, so the strategy you sign your name to actually ships. The signals below are checkable before we touch a system.

What actually ships

A delivery plan and the build behind your roadmap: data models, pipelines, or the AI feature itself, with a runbook the client's team can operate after you rotate out. Not a slide on strategy.

How we think about build vs buy →

dbt Labs Registered Partner

Analytics and transformation work built on the standard a real data team recognizes, not a bespoke setup only we can maintain.

Published research

We publish our work on AI agent reliability and evaluation, including AgentDoctor. The depth behind the delivery is public, not a claim.

The person who scopes it builds it

Nine years across data engineering, cloud infrastructure, and AI. Senior-led throughout, with Domain Methods delivery capacity when a build needs more hands, never a junior handoff.

Secondary resource

Readiness sprint one-pager.

Use this as the short explanation when a CEO, COO, or board sponsor asks why the roadmap needs a technical inspection before buildout.

Download the complete one-pager

A fuller advisor-facing brief with sprint triggers, required inputs, data/system inspection areas, outputs, and green/yellow/red decision rules.

Download one-pager
Problem

The leadership team has AI or data priorities, but nobody has verified whether the current stack can support the first implementation.

Sprint question

Which initiative can ship first, what blocks it, and what needs to be fixed before the client commits more budget?

Inputs

Roadmap hypothesis, current stack, sample reports, data model notes, system access, and the executive decision the advisor needs to support.

Outputs

System map, readiness score, constraints, first-win scope, and the 30-90 day implementation path behind the advisor's recommendation.

What the client gets

Technical evidence the roadmap can stand on.

System map

A practical map of the systems, data flows, ownership gaps, and dependencies that affect the roadmap.

Feasibility score

A clear score across data availability, integration complexity, operational risk, and likely first-win value.

First-win scope

A narrow implementation path the client can fund before committing to broader team buildout or transformation spend.

How your client's data is handled

What we commit to in writing, before any access.

Your client's data and systems stay inside a tightly scoped engagement, with the controls below written into the SOW before anyone touches a system.

Mutual NDA before access

Your paper, the client's, or ours, whichever you prefer. Signed before anyone touches a system.

Read-only by default

No writes to production systems unless the scope authorizes it in writing.

Engagement-scoped credentials

Provisioned at kickoff, revoked at handoff. No long-lived access tokens.

Deletion at sign-off

All exports, screenshots, and working files deleted within 7 days of handoff.

Mutual non-solicit

MLDeep does not pitch advisory, strategy, or ongoing roadmap work to your client. The technical validation is the whole scope.

Partner FAQ

What fractional advisors ask before booking.

What stacks have you actually shipped against?
Data warehouse: Snowflake, BigQuery, Redshift. Transformation: dbt (Registered Partner). Orchestration: Airflow, Dagster. BI: Looker, Sigma, Mode, Metabase. CRM/RevOps: Salesforce, HubSpot. Infrastructure: Terraform, AWS, GCP. AI: production agents on the Claude and OpenAI APIs. 9 years of practitioner work, not slide decks.
What does a Readiness Sprint cost?
Partner-Branded AI/Data Readiness Sprints typically land between $10,000 and $15,000 fixed, with a 2-week delivery window. Written into the SOW before kickoff. No hourly billing, no scope creep.
What does the deliverable look like, and can I co-brand it?
A system map, a feasibility score across data availability/integration complexity/operational risk/likely first-win value, named constraints, and a first-win scope. Format is a polished PDF you can co-brand or forward to client leadership unchanged. Sample available on request.
What if you are unavailable mid-sprint?
MLDeep is a single-practitioner consultancy, but Domain Methods is a formal delivery partner for capacity extension. If a sprint hits a hard timeline constraint, Domain Methods consultants can be brought in under MLDeep's brand and direction. You will know about this option before signing the engagement letter.
How do you protect my advisor seat with the client?
Mutual non-solicit in every engagement letter. MLDeep does not pitch advisory, strategy, or ongoing roadmap work to your client. The technical readiness sprint is the entire scope. You keep the executive relationship and the follow-on recommendation.
Do you carry SOC 2 or E&O insurance?
No. Single-practitioner consultancy. Trust controls in writing: mutual NDA, read-only access by default, engagement-scoped credentials, single-operator access (no subcontractors), engagement-end deletion within 7 days, liability capped at fees paid.
Next step

Bring one executive roadmap question.

We will help you decide whether a readiness sprint can strengthen the recommendation, narrow the build scope, or reveal that the client should fix foundations first.

Responds within one business day · calls overlap 17:00-22:00 IST / 07:30-12:30 ET · back to partner hub.