We help PE-backed CXOs leverage AI in their organizations to increase operational efficiency and validate new revenue streams, with outcomes that show up in EBITDA and exit multiples.
How we do it
Leaders bring their AI hunches. Our forward-deployed engineers embed in their teams to run discovery, create proposals, and build what leadership approves, then support handoff or scale. Our remote teams are supported by co-located product and technology leadership.
Who We’ve Worked With
Top-10 Megafundbacked portfolio company
Leading UScrypto exchange
4 Fortune 500sserved
12 unicornsand 170+ startups
We don’t scale AI until it reaches Workflow-User Fit
Workflow-User Fit (WUF) is the point where AI fits both the workflow it enters and the people who run it. It rarely happens on day one, so we iterate toward it. We discover and understand your workflows, automate them, gather real-world feedback, and keep refining until the fit is there.
Five stages of working together, planned around your business milestones
01AlignGoal, success measures and scope agreed
02DiscoverIdeas found, scored and ranked with you
Gate: first proof
03ProveA first version in users’ hands
Gate: scale-up
04ScaleMore workflows live, quarter by quarter
Discover, Prove and Scale repeat for each new workflowGate: each quarter
05HandoffYour team runs and extends it
At each gate you decide whether to continue, so budget is committed one stage at a time.
Our Forward Deployed Pods embed with your team
Every engagement is run by a forward-deployed team: senior leaders close to your business, engineers inside your workflows, and hours that overlap with yours.
Colocated CTOsPeople who have run these workflows before, with a direct line to your leadership while the work happens.
Global FDEsEngineers who embed with your team until the workflow sticks, led by a core of 20 partners with 14+ years at the firm.
Cross-functional teamBusiness analysts, data specialists and engineers in one team, working to each department’s standards.
4+ hours of daily overlapWorking hours that overlap with yours every day, so decisions never wait overnight.
Every pod follows the same operating principles, from the first workflow to handoff.
Impact analysis firstKnock-on effects in connected systems are checked before each change.
ROI-led and measuredHighest-return work first, each tied to a success measure and tracked.
Built in your systemsYour existing tools, data and repositories. The code stays yours.
Engineering-gradeCode your CTO and engineering team will sign off on.
Embedded with youInside your organisation, working alongside your people day to day.
Iterate more affordablyRun more experiments for the same budget, and keep the ones that show up in EBITDA.
Every stage runs within your security, governance and compliance rules.
Communities that bring experienced leaders and operators to the table
Our communities connect us with technology leaders and PE-backed operators who share practical lessons from building and adopting AI.
Fractional CTOs with the domain knowledge your workflow needs
When a workflow needs domain depth, we bring in a senior technology leader who has done this work before. They come from CPTOFracs and work alongside our forward-deployed engineers, with at least four hours of daily overlap.
An invite-only community where operators at PE-backed companies share what worked with AI, what failed, and why. Only 7% of portfolio companies have reached enterprise-scale AI deployment (FTI, 2026 PE AI Radar). The table exists so operators can learn from peers who are further along.
Who it is for
Operators, executives and CXOs at PE-backed companies of roughly $100M to $500M in revenue who own the AI-driven operational transformation
Operating partners and value creation leads at the funds behind those companies
Monthly roundtablesSix to eight seats, one real problem worked through by people under the same pressure.
02
Lightning talksTen to fifteen minutes. One operator, one story: what worked, what didn’t, and why.
03
A running group between sessionsA private room to keep the conversation going after each roundtable.
Run by Mannan Amin, Co-Founder & President of Tintash. Chatham House rules apply, and vendors don’t pitch at the table, including Tintash.
Healthcare leaders and AI builders, sharing what actually works
An invite-only community focused on applying agentic AI in healthcare. Healthcare leaders and AI developers share real-world lessons on what works and what doesn’t, through roundtables, webinars and lightning talks. No sales, no fluff. Tintash powers it.
No pitch, no slides. Just a relaxed chat with Mannan to get to know you, hear which workflows are slowing things down, and see if there’s a way we can help.
Document-heavy work such as compliance checks, verification and regulatory research scales linearly with specialist time, across many formats and languages, and every decision has to stand up to audit.
Your teams are rightly focused on serving existing customers, which leaves little room to experiment. We work as the embedded team that tests new ideas and turns proven wins into systems your people own.
What we deliver
Multi-agent extraction across document types and languages
Deterministic rules, so every pass or fail decision is explainable
A reviewer workflow with the extraction and rule trace attached
A complete audit trail for enterprise compliance review
Avetta (EQT-backed) · Top 10 Megafund Portfolio CompanyAI-Enabled Document ProcessingBuilt with Claude
AI-Powered Document Verification for a Top 10 Megafund Portfolio Company
Manual supplier document verification was capping how fast the business could grow. Forward-deployed engineers built a Claude-powered Batch Verification Service with full reviewer auditability.
Results
~95%field-level extraction accuracy on text PDFs
4,500 hrssaved per 100,000 verifications, up to
14 · 6document types · languages, across EMEA and North America
25concurrent verifications, up to
The Challenge
Avetta verifies supplier compliance documents for more than 130,000 businesses across 120 countries. As submission volumes grew, the legacy process became expensive and hard to scale.
Every document needed manual extraction followed by heavy specialist review, across multiple languages and document types. Verification throughput, not demand, had become the constraint on the business.
What Tintash Delivered
We deployed forward-deployed engineers into the team that owned the workflow and built the Batch Verification Service around how reviewers actually work.
Multi-agent extraction pulls fields at the level a specialist would. Deterministic compliance rules then adjudicate those fields, so each decision is explainable. Anything ambiguous goes to a human reviewer with the extraction and the rule trace attached.
The engagement closed with a structured handoff to the client team.
Business Impact
What changed
~95% field-level extraction accuracy on text PDFs
Up to 4,500 hours saved per 100,000 verifications
14 document types in 6 languages across EMEA and North America
Up to 25 concurrent verifications
Every decision traceable for enterprise compliance review
Embedded teams that turn business ambition into automation you own
We embed forward-deployed teams of business analysts, data specialists and engineers into your operations.
Five stages
At each gate you decide whether to continue
Budget is committed one stage at a time. Discover, Prove and Scale repeat for each new workflow, and every stage runs within your security, governance and compliance rules.
01AlignGoal, success measures and scope agreed.
02DiscoverIdeas found, scored and ranked with you.Gate: first proof
03ProveA first version in users’ hands.Gate: scale-up
04ScaleMore workflows live, quarter by quarter.Gate: each quarter
05HandoffYour team runs and extends it.
Discover
Deciding what to build
Business discovery: what to automate and why, guided by the goals agreed in Align.
Typically 2 to 4 weeks for the first Discovery
DiscoverInterviews with every team involved
DefineMap the process and group pain points
IdeatePropose solutions for each pain point
PrioritiseScore each solution; you choose what to build
Every solution is scored with you on five criteria
Adapted from RICE, plus measurability.
RelevanceDoes it move a success measure?
ImpactTime, cost or risk it removes
ConfidenceHow sure we are it will work
EffortWhat it takes to build and run
MeasurabilityCan the gain be proven with data?
Discovery is led by a business solutions lead, a technical solutions lead and a data specialist; engineers join when we build.
Prove and Scale
Build and iterate. No automation is perfect on day one.
This runs first in Prove, then for each new workflow in Scale. Each version is released, measured and refined until it works in practice.
01Technical feasibilityCheck knock-on effects on connected systems and SOPs.
02BuildIn your own tools, data and repositories.
03ReleaseA first version in users’ hands, fast.
04MeasureTrack results against the agreed success measures.
05IterateRefine with real use, then release again.
06Embed in operationsWe run new tech and SOPs alongside your team.
Systems, code and new SOPs are yours from the first commit. At the Supplier Readiness client, handoff meant 20+ turnover sessions, all code in the client’s own repositories, and a system the client’s own team has since extended.
Tintash is a tech services firm that helps companies build and scale through specialized studios and AI-driven workflows.
Trained at the d.school. Wired to prototype.
Two Stanford alumni met over breakfast at Hobee’s in Palo Alto in 2008 and decided the best way to learn what works is to build it. They wanted to create opportunities for talented professionals in their home country, and they built the firm around experimentation rather than specification. Design thinking and lean-startup method applied to client work, not just to internal decks.
Eighteen years later that instinct is the reason forward-deployed engineering works for us: we would rather run a scoped pilot inside a live workflow than write a strategy document about it. We start most relationships with a small project, relationship prototyping, because the fastest way to know whether two teams work well together is to ship something small together first.
Our teams are deliberately balanced and diverse, organised around product vision and impact rather than billable-hours utilisation. We run our own performance platform, Camaradly, to keep that honest.
Who We’ve Worked With
Top-10 Megafundbacked portfolio company
Leading UScrypto exchange
4 Fortune 500sserved
12 unicornsand 170+ startups
The five values at the heart of our work.
Have faithCommit to the hard version of the problem before you can see the whole path.
Be proactiveSurface the issue before it becomes a status update.
AkhlaqCharacter and conduct. How we treat people when the project is under pressure.
PrototypeBuild the smallest real thing rather than argue about the abstraction.
ShokuninThe craftsman’s obligation to the work itself, independent of who is watching.
90.7%Net promoter score
98%Employee retention
18 yrsBuilding products
5Countries
8Practice areas
TealOperating model
From a breakfast table to five countries.
Eighteen years on, the through-line is unchanged: build the smallest real thing, put it in front of the people who have to use it, and let the evidence decide what happens next.
2008
Founded at Hobee’s, Palo Alto
Two Stanford alumni, d.school-trained, start a firm built on prototyping rather than specification.
2012
Studios take shape
Practice areas form around apps, games and emerging platforms, the range that later feeds the enterprise work.
2018
Scale clients arrive
Fortune 500 programmes and unicorn-stage engagements: AR commerce, mass transit, superapp re-architecture.
2021
Protocol engineering
On-chain systems where correctness is not negotiable: oracles, exchange integrations, contract audits.
2024
Forward-deployed AI
Engineers embed inside client workflows. Workflow–User Fit becomes the operating principle.
2026
Enterprise AI practice
Document verification, workflow automation and certified data products inside PE-backed portfolio companies.
Founded by Stanford alumni in 2008, Tintash has built products across startup, unicorn and enterprise environments.
That experience now powers our focus on Enterprise AI for PE-backed companies.
Where It Started
2008
2008. Hobee’s, Palo Alto.
It all started in 2008 over breakfast at Hobee’s in Palo Alto, where two Stanford alumni decided to prototype working together.
Trained at the Stanford d.school, they believed in learning by doing and set out to create opportunities for top talent in their home country.
That early instinct to prototype, learn from real users and keep iterating became part of how Tintash approached product building over the years.
Building Across Technology Cycles
From Early-Stage Products to Scaled Platforms
Over the next 16 years, Tintash worked across different stages of company and product development, from helping founders take ideas from zero to one to building and scaling products for unicorns and enterprise environments.
The technologies and industries changed. The underlying work remained product building: understanding users, navigating ambiguity, shipping working software and improving it through real-world feedback.
170+Startups
12Unicorns
4Fortune 500 companies
Leading USCrypto exchange
Product-Building Journey before 2024
Before Enterprise AI became our primary focus, Tintash built across three specialist practices.
Zero-to-One
Ideas to launched and scaled products.
170+Startups served
12Unicorns
2 moTypical MVP
Games & Interactive
Games and interactive experiences built for scale.
50M+Downloads
#3US App Store peak
4.9★Player ratings
Web3
Blockchain products, protocols and infrastructure.
$25KBug caught in 6 min
6Protocol clients
24/7Oracle uptime
2024 Onward
Enterprise AI Became Our Primary Focus
By 2024, the product-building discipline developed over years of working across startup, unicorn and enterprise environments had become the foundation for Tintash’s next chapter.
As AI moved from experimentation into real operational workflows, we brought that same approach to Enterprise AI: understand the problem, work closely with users, validate quickly and scale what creates measurable value.
Today, our forward-deployed engineers embed with client teams to turn leadership’s AI hunches into validated opportunities and working systems.
