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ThinkscoopEngineering
EngagementsCapabilitiesWorkApproachNotesAboutStart a build
EngagementsCapabilitiesWorkApproachNotesAboutStart a build

Thinkscoop Engineering

Senior engineers, AI augmented. Not AI washed.

Engagements

  • AI MVP Sprint
  • AI Integration Pod
  • Agentic Workflow Build
  • Embedded AI Pod
  • All four, with prices

Capabilities

  • Retrieval and context
  • Evaluation and quality gates
  • Agent orchestration
  • Guardrails and escalation
  • Observability, cost and drift
  • Product and platform engineering

The practice

  • Engineering home
  • Delivered work
  • How we work
  • Engineering notes
  • About the practice
  • Questions we get asked
  • Start a build

Reach us

contact@thinkscoopinc.com

Other practices

  • Business Applications
  • Growth
  • Thinkscoop, the parent company

Legal

  • Privacy policy
  • Terms
  • Cookies
Thinkscoop Technologies LLPTeam based in India. Clients across the US, Australia and the UAE.
Engineering/About

The practice.

What this is, how it is set up, and the six positions that decide what we build and what we turn down.

Thinkscoop Engineering is the AI and product engineering practice at Thinkscoop Technologies LLP. Two sibling practices sit alongside it: Business Applications, which works on enterprise platform estates, and Growth, which works on brand and go-to-market. This one builds software.

The team is based in India and the clients are in the United States, Australia and the Gulf. There is a live overlap with United States east coast mornings and a full working overlap with Australia and the UAE, and the rest of the day runs on written handoffs. That is stated plainly rather than presented as a follow-the-sun promise, because the honest version of it is that the discipline of the handoff note is what makes it work.

The practice sells four engagements and publishes the price of all four. It refuses work it thinks should be bought rather than built, and it says where its own approach stops working on every capability page. That is not modesty. It is the fastest way for a technical buyer to find out whether this is a fit, which is the only thing either of us needs from a website.

$engineering / positions

Six things we think are true.

P1

AI augmented, not AI washed

Our engineers use AI tooling the way they use a compiler and a debugger. Every line is reviewed, tested and owned by the person whose name is on the pull request. The distinction matters because the industry is currently full of both, and only one of them can answer a question about its own code.

P2

Most of an AI product is not AI

It is a schema, an auth story, a queue, a migration and a deploy pipeline. That work decides whether anyone can use the thing, and it is where projects that demo well quietly die. We price it in rather than discovering it in week nine.

P3

Measurement before opinion

Nothing is built against an unmeasured target. The labelled set, the threshold and the consequence of missing it are agreed in week one, so every later argument about quality is a number that moved rather than a debate somebody wins by seniority.

P4

The exit is designed at the start

Your cloud account, your repository, your credentials, documentation and evaluation coverage as contractual deliverables. A supplier who is hard to leave is a supplier you stop trusting, usually about a month after you notice.

P5

Escalation is a product surface

The handoff to a human is not the failure path. It is the part that decides whether the automation is worth having, and it gets designed before the happy path rather than bolted on after the pilot.

P6

Say the limit out loud

Every engagement scope names what it excludes and every capability page names where the approach breaks down. A buyer who finds the limits on the website is not surprised by them in month three, and month three is when surprises get expensive.

$engineering / fit

Three fits, and three that are not.

In that order. Reading the second three first is a reasonable use of your time.

Fit

You have real data and a real decision to make

There is a dataset, a workflow or a corpus that exists today, and a question about it that has a deadline attached. That is the shape of engagement that goes well.

Fit

You want the system in your own account

Your cloud, your repository, your evaluation suite. If the goal is to end up owning a capability rather than renting one, everything here is arranged for that.

Fit

Somebody technical will read what we write

Pull requests get reviewed by your engineers, architecture notes get argued with, and the evaluation threshold gets challenged. That is the engagement working as intended.

Not a fit

You need a certified vendor today

The SOC 2 Type I audit is in progress and not yet certified. We build to your controls, work inside your tenancy and document the data flows, and if your procurement gate is a completed report, that gate is not open yet.

Not a fit

You want a fixed price for an undefined scope

The AI MVP Sprint is fixed scope and fixed fee because the scope is fixed in week one. A large build against a brief that is still moving is priced in phases, with a stopping point, or it is not priced at all.

Not a fit

You want headcount you manage directly

The Embedded AI Pod is a capability with a lead accountable for outcomes and a monthly written report. It is not billed per person per hour and we will not present it as something it is not to fit a procurement category.

$engineering / terms

What is the same on every engagement.

First response

One working day, from a person, not a form autoresponder

Contract to first commit

5 business days

Code ownership

Yours in full, assigned in the contract, from the first commit

Repository

Yours. We work on branches through your review process

Infrastructure

Your cloud account, credentials issued and revocable by you

Overlap

Mornings US eastern, full days for Australia and the Gulf, async handoffs for the rest

Paperwork

NDA, IP assignment and security review before kickoff, not after

Currency

USD

The stage by stage method and the full list of work we turn down are on how we work.

If that reads like the team you want.

Send the problem. An engineer reads it and you get a written view within one working day, including the version where the answer is no.

Start a build