Imran Chatur

Work & experience

I design AI for enterprises, and build the firms that sell it.

15+ years connecting enterprise technology, commercial decisions and the people doing the work.

Imran Chatur
Imran Chatur · Greater Vancouver, BC

My background is in industrial engineering and operations management, and I am a certified project manager. I hold technology certifications from Anthropic, Salesforce, MuleSoft, Databricks and Marketo. I have been a technology project manager, architect, delivery leader and revenue officer. I love technology and building solutions to complex problems.

My experience

Selected work

A few problems I’ve worked on.

Enterprise workflow02

Redesign onboarding around what you own.

A connected customer workflow using the systems already in place.

Read the approach

The problem

New customers waited weeks for first value across email, re-keyed data and systems that did not talk to each other.

Design decisions

  • Native first, build last
  • One master record per entity
  • Groundwork sequenced before AI
  • Scoring on weights the owners control
Consulting firms03

Connect the sale to the work that follows.

Clear ownership of relationships, forecasts and margin across sales and delivery.

Read the approach

The problem

Selling and delivering each assumed the other held the relationship, the forecast and the margin, and all three slipped in the gap.

Design decisions

  • Every relationship given a named owner
  • The architect in the room before the deal
  • Growing a first project into a program
  • Every stage given an owner and a measure
Browse all 50 pieces of work

50 pieces of work

Continuous control monitoring that raises only the exceptionsEnterprise

Comparing controls across systems and data took manual effort, time and judgement, long after the event.

Design decisions

  • Flag the exceptions, let a person decide, remember the verdict
  • Groundwork sequenced before AI
  • Training and adoption staffed as part of the build
Agents that answer a team's questions from its own documents and recordsEnterprise

The answers a team needed lived in unstructured material, and the appetite for AI outran any agreement on where to start.

Design decisions

  • Search that keeps permissions and cites its sources
  • Native first, build last
  • Scoring on weights the owners control
Certification made a condition of promotionFirm

Skill was asserted rather than evidenced, so the firm could not show depth to a buyer or find its own gaps before a pursuit exposed them.

Design decisions

  • A credential made a condition of the next role
  • Credentials pursued for the access they open
Career paths set by competency, and a growth plan for every personFirm

People could not see what the next role asked of them, and the firm only learned someone was leaving when they resigned.

Design decisions

  • Every role given its competencies and the level each one needs
  • A development plan for every person, reviewed on a cadence
A repeatable way to win work alongside a platform vendor's field teamsFirm

Joint pursuits depended on who was in the room, and the vendor people who decide which firms get invited sat across an organization nobody had mapped.

Design decisions

  • Co-selling through the vendor's field teams
  • The partner ecosystem mapped by role, and worked on a cadence
  • The architect in the room before the deal
  • Every relationship given a named owner
An AI-enabled configure-price-quote application that turns requirements into formal sales proposals with all supporting documentsEnterprise

Turning a customer's requirements into a valid product specification, with prices and a delivery schedule, was slow specialist work repeated for every opportunity.

Design decisions

  • A purpose-built decision application on shared data
  • Groundwork sequenced before AI
  • Phase one fixed, later phases as ranges
Plain-language analytics on governed definitionsEnterprise

Teams wanted to ask questions of their data directly, without an assistant returning a different number from the dashboard.

Design decisions

  • Search that keeps permissions and cites its sources
  • Groundwork sequenced before AI
  • Demonstrated on the business's own live data
Spreadsheet decision models rebuilt as applications the owners still controlEnterprise

Recurring decisions ran on spreadsheets that had hit their ceiling, fed by hand from separate systems, and the people who built them needed to keep control.

Design decisions

  • A purpose-built decision application on shared data
  • Scoring on weights the owners control
  • The pilot proves it, production hardens it
  • Training and adoption staffed as part of the build
A delivery method defined and taught, so every engagement ran the same wayFirm

Quality tracked whoever happened to be staffed, because how the firm ran an engagement was learned on the job and never written down.

Design decisions

  • The method written down and taught
  • Assets harvested from work already done
  • The architect in the room before the deal
A content and demand engine aimed at the people who refer the workFirm

Reaching the people who send work was done by hand, one relationship at a time, and stopped whenever the team got busy.

Design decisions

  • A value exchange that turns interest into a conversation
  • Proof captured at the close of the work, while it is still true
  • The partner ecosystem mapped by role, and worked on a cadence
  • A second route to market the firm owns outright
An architecture review board that scores designs before they are builtFirm

Architectural decisions were made inside each engagement and only questioned once something broke, by which point changing them meant rebuilding.

Design decisions

  • Design reviewed and scored before the build starts
  • The method written down and taught
An outbound play run end to end, from target list to qualified conversationFirm

Almost everything arrived through one channel, and the firm had never tested whether it could generate and qualify its own leads.

Design decisions

  • A second route to market the firm owns outright
  • A value exchange that turns interest into a conversation
  • Every stage given an owner and a measure
A paid discovery packaged so a partner's own sellers could use itFirm

A partner's sellers could not size their own deals without technical depth they did not have.

Design decisions

  • A fixed-scope plan as the way in
  • An offer designed for the partner's seller as well as the client
  • Co-selling through the vendor's field teams
Agreements drafted from a clause library for a person to approveEnterprise

Agreements were assembled by hand from templates and records, and a change to the underlying rules meant reworking every one.

Design decisions

  • A tool layer (MCP) over the core systems
  • Documents turned into data, checked against known answers
  • Groundwork sequenced before AI
Technical documents turned into structured dataEnterprise

Documents held detail the business needed as data, and reading them by hand was both the bottleneck and the cost.

Design decisions

  • Documents turned into data, checked against known answers
  • Cost built up from the units that drive it
  • The pilot proves it, production hardens it
A single estimating method, and every scope change traced to its causeFirm

What was sold and what could be built kept drifting apart, and nobody could say whether the cause was the estimate, the design or the client.

Design decisions

  • One estimate template, used by everyone who scopes
  • Every change traced to a cause, and the cause given an owner
  • Losses reviewed, and the review fed back into pricing
An agent that assembles the evidence behind an operating eventEnterprise

The context needed to understand an operating event sat across several systems, and someone assembled it differently every time.

Design decisions

  • A coordinating agent with specialist agents
  • A tool layer (MCP) over the core systems
  • Set limits on what AI can decide
  • Demonstrated on the business's own live data
Long-form answers drafted from a governed document setEnterprise

Answering a long, standard question set meant finding the right passage in a large document library by hand, every time.

Design decisions

  • Search that keeps permissions and cites its sources
  • Documents turned into data, checked against known answers
Field work captured once on the device, straight into the core systemEnterprise

Work done away from a desk was recorded on paper and transcribed later, so errors surfaced late and billing waited on all of it.

Design decisions

  • One master record per entity
  • A purpose-built decision application on shared data
A go-to-market and revenue model for a growing firmFirm

Revenue depended on a few leaders' relationships, pricing was a single blended rate, and the partner channel was informal.

Design decisions

  • Priced by role, with the discount thresholds set in advance
  • Co-selling through the vendor's field teams
  • Offers packaged from past work
Inference placed on owned hardware, so the material never leaves the network it sits onEnterprise

Some of the material an assistant needed to read could not be sent to a service outside the network it sat on, and the work still had to run.

Design decisions

  • Cost built up from the units that drive it
  • Keep the analysis layer apart from the everyday tools
  • A tool layer (MCP) over the core systems
Inbound demand scored and routed on the systems already in placeEnterprise

Inbound arrived from several sources into a system of record that was staying, and deciding who followed up was manual.

Design decisions

  • One master record per entity
  • A tool layer (MCP) over the core systems
  • Groundwork sequenced before AI
A memory layer rebuilt as one store with one writer, so it stops disagreeing with itselfEnterprise

Several stores of the same knowledge, built at different times with no owner, contradicted each other, and the warning that mattered did not surface when it mattered.

Design decisions

  • One master record per entity
  • Search that keeps permissions and cites its sources
  • Flag the exceptions, let a person decide, remember the verdict
  • A remembered rule decays until someone re-confirms it
On-premises anomaly detection that sends on only the signalsEnterprise

Continuous capture produced more material than anyone could review, on sites where the connection could not carry it in time.

Design decisions

  • Process on site where the connection is weak
  • Flag the exceptions, let a person decide, remember the verdict
  • Cost built up from the units that drive it
  • The pilot proves it, production hardens it
Customer onboarding redesigned, then automated with what was already ownedEnterprise

New customers waited weeks for first value across email, re-keyed data and systems that did not talk to each other.

Design decisions

  • Native first, build last
  • One master record per entity
  • Groundwork sequenced before AI
  • Scoring on weights the owners control
Past work packaged into offers that stack on one engagementFirm

The firm sold hours, so every deal was a custom negotiation, and buyers wanted a defined outcome before they would commit.

Design decisions

  • Offers packaged from past work
  • Offers designed to stack on a single engagement
  • A fixed-scope plan as the way in
  • Priced by role, with the discount thresholds set in advance
Partner-program credentials chosen for the deals they openFirm

The rating a partner carries decides which conversations it is shortlisted for, and the firm had not chosen which ones to go after.

Design decisions

  • Credentials pursued for the access they open
  • Proof captured at the close of the work, while it is still true
  • The partner ecosystem mapped by role, and worked on a cadence
A planning side and a deterministic execution side, with one database as the only contractEnterprise

Judgement and execution ran in the same place, so a decision could not be reviewed apart from the mechanism that acted on it, and nothing stopped it acting.

Design decisions

  • A coordinating agent with specialist agents
  • Set limits on what AI can decide
  • Flag the exceptions, let a person decide, remember the verdict
  • One master record per entity
A data and AI platform chosen based on the use casesEnterprise

Several platform decisions had to be made together, each one constraining the others, before any use case could be built.

Design decisions

  • Groundwork sequenced before AI
  • The pilot proves it, production hardens it
Positioning rebuilt from market research, segment by segmentFirm

The firm sold across ecosystems with no focus, so neither buyers nor technology vendors saw it as expert in any, and its message read like everyone else's.

Design decisions

  • The position chosen from research
  • Each segment matched to the product mix it buys
  • Offers packaged from past work
A practice run on numbers agreed before anyone was held to themFirm

The firm had no agreed measure of margin, capacity or how well it forecast, so a busy quarter and a profitable one looked the same until both were over.

Design decisions

  • The measures agreed before anyone is held to them
  • Every stage given an owner and a measure
A delivery practice scaled by dividing it along what each team ownsFirm

Demand arrived faster than delivery could absorb it, and the customer, the platform and the build were each looked after by whoever was nearest.

Design decisions

  • Teams divided by what each one owns, end to end
  • Every relationship given a named owner
  • Offers packaged from past work
A technical presales function stood up from scratchFirm

The firm architected its solutions after the contract was signed, in delivery.

Design decisions

  • The architect in the room before the deal
  • The method written down and taught
Pricing moved off a single blended rate onto a role-based rate cardFirm

A single blended rate meant every negotiation discounted the whole team, and margin moved without anyone deciding that it should.

Design decisions

  • Priced by role, with the discount thresholds set in advance
  • Losses reviewed, and the review fed back into pricing
A narrow product bet, argued from why the platform pitch kept losingFirm

The platform pitch everyone wanted to sell kept losing to cheaper, narrower products.

Design decisions

  • A narrow bet argued in place of the broad vision
  • Losses reviewed, and the review fed back into pricing
  • Assets harvested from work already done
A satisfaction and reference loop that turned finished work into proofFirm

Good work ended quietly, and nobody asked for the evidence that would have won the next one.

Design decisions

  • Proof captured at the close of the work, while it is still true
  • Credentials pursued for the access they open
Outbound prospecting rebuilt as data enrichment and model-assisted fit scoring, landing in the system of recordOwn practice

Deciding who to approach meant researching every account and contact by hand, and the reasons behind each choice sat in scattered lists nobody could sort or reuse.

Design decisions

  • Scoring on weights the owners control
  • One master record per entity
  • Set limits on what AI can decide
A read-only tool layer over a system with no interface, with everything it returns marked untrustedEnterprise

The material an agent had to read sat behind a system with no interface to build against, and anything it read could carry instructions of its own.

Design decisions

  • A tool layer (MCP) over the core systems
  • Read-only discovery on the live systems
  • Set limits on what AI can decide
  • Everything a tool reads back is treated as untrusted
A relationship system of record with a tool layer an agent can read and writeEnterprise

The record of every relationship and open conversation ran in spreadsheets, kept by hand from several places, with no history and nothing an assistant could read.

Design decisions

  • Native first, build last
  • One master record per entity
  • A tool layer (MCP) over the core systems
Client retention designed as a motion, from first project to programFirm

Repeat work depended on somebody remembering to ask, long after the team that earned the right to ask had moved on.

Design decisions

  • Growing a first project into a program
  • Offers designed to stack on a single engagement
  • Every relationship given a named owner
A library of reusable assets harvested from finished engagementsFirm

Every engagement rebuilt the same foundations, and the reusable parts stayed on the projects that first paid for them.

Design decisions

  • Assets harvested from work already done
  • Offers designed to stack on a single engagement
A commercial organization designed by role and sequenced by hireFirm

Everyone sold a bit of everything, so no part of the cycle had a specialist and hiring was a reaction to whoever left.

Design decisions

  • The organization designed by role, the hires sequenced to demand
  • Every stage given an owner and a measure
  • The method written down and taught
A sales cycle defined end to end, owned stage by stage and taught to the teamFirm

How work was won lived in habit and in a handful of people, so there was nothing to onboard a new seller with.

Design decisions

  • Every stage given an owner and a measure
  • The method written down and taught
  • Every relationship given a named owner
An operating model that names who owns what between selling and deliveringFirm

Selling and delivering each assumed the other held the relationship, the forecast and the margin, and all three slipped in the gap.

Design decisions

  • Every relationship given a named owner
  • The architect in the room before the deal
  • Growing a first project into a program
  • Every stage given an owner and a measure
Early warning of delivery risk, with the evidence attachedEnterprise

The early signs of a slipping schedule sat in correspondence and reports weeks before they reached a status report.

Design decisions

  • Flag the exceptions, let a person decide, remember the verdict
  • Search that keeps permissions and cites its sources
  • Set limits on what AI can decide
  • Native first, build last
A generalist firm repositioned around one specialtyFirm

The firm competed on breadth, and the vendor field teams who decide which partners get invited did not know it existed.

Design decisions

  • Co-selling through the vendor's field teams
  • Growing a first project into a program
  • A fixed-scope plan as the way in
Where AI belongs across existing technologiesEnterprise

The business wanted to know where AI belonged across systems it did not intend to replace.

Design decisions

  • Read-only discovery on the live systems
  • Rank every system by how an agent can reach it
  • Reach it live before copying it
  • Keep the analysis layer apart from the everyday tools
Wins and losses read back into how the next one is pricedFirm

Pursuits were lost for reasons nobody wrote down, so the same causes kept recurring and instinct set the price.

Design decisions

  • Losses reviewed, and the review fed back into pricing
  • Priced by role, with the discount thresholds set in advance
An end-to-end workflow rebuilt as orchestrated agentsEnterprise

Every handoff along a core workflow was re-keyed and re-interpreted, and the status of any item had to be asked for.

Design decisions

  • An evidence pack the business corrects in writing
  • A constraint register with an owner and a cost
  • One master record per entity
  • A tool layer (MCP) over the core systems
Explore the connections as a mapThe same work, shown through its connections.

Experience

Where I’ve done the work.

  1. Lift Consulting

    Sep 2014 – present Principal Primary technologies Anthropic Grok Salesforce Google Cloud HubSpot Zendesk SAP Infor
    • Advise digital-services organizations on revenue growth and consulting operations.
    • Design and run digital-transformation programs, directly for enterprises and for firms’ clients.

  2. Fastloop

    Nov 2023 – Oct 2026 Chief Revenue Officer · engaged through Lift Consulting Primary technologies Anthropic Google Cloud Superblocks

    Built the go-to-market and technical-architecture function for enterprise AI work.

    • Worked directly with Google Cloud to position the firm as partner of choice in its vertical, across 10+ enterprise pursuits.
    • Ran technical discovery and architected the solution before close, and owned the co-sell motion and the executive narrative.
    Two more
    • Built the technology specialization on Google Cloud’s data and AI stack and on Anthropic, and the vertical specialization in heavy industry.
    • Designed agentic, generative, vision and edge systems against each client’s existing estate, every one costed and evaluated against the real workflow.

  3. Groundswell Cloud Solutions

    Mar 2018 – Aug 2023 Acquired by GyanSys in 2022. Chief Revenue Officer · 2020 – 2023 VP, Professional Services · 2018 – 2020 Primary technologies Salesforce MuleSoft

    A top-25 North American Salesforce partner. I was CRO through its acquisition.

    • Carried a $30M number as CRO, at a 68% win rate, through the acquisition.
    • Scaled the professional-services practice from 20 to 100 people on codified playbooks, role-based enablement and one certification model.
    Two more
    • Built the customer-engagement strategy end to end, and the sales, marketing and engagement function under it.
    • Created the technical assurance team, the architecture review board and the guidelines that governed work across the practice.

  4. Traction on Demand

    Sep 2010 – Sep 2014 Presales Director & Solution Architect Primary technologies Salesforce Marketo

    Later acquired by Salesforce, after my departure.

    • One of the firm’s earliest hires, and lead solution architect on its most strategic accounts.
    • Led the team that closed the two largest accounts, more than 20% of revenue.
    One more
    • Built the technical presales process and the discovery playbooks under it.

What I’ve learned

Nine principles I come back to.

  1. No quantified outcome means no project.

    A real, measurable outcome with accountability behind it is what enables change. Without an outcome, don't start.

  2. Process leads, data and technology follow.

    How work is done is what matters most. Any meaningful, long-lasting change has to be rooted in evidence and solve for a constraint.

  3. People matter most.

    I have worked with some amazing people, both inside enterprises and inside project teams. Technology accelerates people, not the other way around.

  4. Don’t pay attention to buzzwords.

    AI changes workflow. Dig into the workflow first, then look to AI to transform it or remove it completely if it is no longer needed.

  5. Enterprise technology alongside AI.

    Custom applications or packaged applications: both have their place inside enterprises.

  6. AI is a layer on a solid foundation, not a substitute for one.

    Without stable source systems and a stable data foundation, AI won't solve for anything.

  7. Intelligence matters less than management.

    Whatever advantages a model has, enterprise leaders do not care. How models are managed and used is what truly matters.

  8. Projects are finite, but change is infinite.

    Once you start, plan to continue. Enterprise technology changes every decade, so design for the business you expect to become, with room to replace the parts that will change.

  9. Develop your vision and ensure there is a leader who is accountable to it.

    Make the future state clear, then name the leader who owns the decisions, the trade-offs and the outcome. A vision without an accountable owner stays a presentation.

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