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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

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  • Thinkscoop, the parent company

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Thinkscoop Technologies LLPTeam based in India. Clients across the US, Australia and the UAE.
Engineering/Engagements/03

Agentic Workflow Build

A multi step workflow that currently runs on people, rebuilt as a system that acts, checks itself, and hands off cleanly when it should not act.

$150k to $300k12 to 16 weeks

Price

$150k to $300k

Duration

12 to 16 weeks

How it is priced

Phased, with a go or no go at the end of the design phase. The range moves on the number of systems the workflow has to write to, the compliance surface, and how much of the process exists only in people's heads at the start.

Who is on it

A delivery lead, senior engineers on orchestration and integration, an engineer on evaluation and guardrails, and a designer on the human review surfaces.

Written for

Mid market and enterprise operations owners where a process spans several systems, runs at volume, and cannot be automated with rules because the inputs are messy.

Why this engagement exists

An agent that can act is a different risk profile from a tool that can answer. The work is less about reasoning and more about authority: what the system is allowed to do without asking, what it must escalate, how a human sees why it decided what it decided, and what the audit trail looks like when someone asks in nine months. We design the escalation path before the happy path, because the escalation path is what determines whether the system is allowed to stay switched on.

What is in scope

  • Process mapping with the people who do the work, ending in a written specification of every step, every exception and every current workaround
  • An authority model: the actions the system may take autonomously, the thresholds above which it recommends rather than acts, and who approves what
  • Connectors to the source and destination systems, with field level access control and idempotent writes
  • Orchestration with explicit state, so every step is inspectable and a run can be replayed rather than guessed at
  • Retrieval over the policy, reference and historical data the decision depends on, versioned so a decision cites the rule that applied on the date it was made
  • Guardrails: schema validation on every output, confidence scoring, and an escalation that arrives with the evidence already assembled
  • The human review surface, designed rather than bolted on, because this is where the process actually lives
  • An immutable decision log, exportable as a single record per case, and monitoring for drift in inputs and outcomes
  • A shadow run against live traffic before anything is allowed to act, with the results reviewed against the agreed thresholds

What you hold at the end

  • The workflow running in your environment, with the authority model enforced in code rather than described in a document
  • Source, infrastructure as code, and the connector layer, all in your repositories
  • The evaluation suite, the shadow run results, and the thresholds each stage passed
  • The decision log schema and an export routine your compliance team can run without us
  • Runbooks for the failure modes, an incident path, and a rollback that returns the process to people cleanly
  • Training material and sessions for the team who will operate it

What this is not

Stated here rather than discovered in month three. Anything on this list can be scoped separately, and we will say what it would take.

  • A promise of a specific automation rate before the shadow run. Anyone quoting one before seeing your data is quoting someone else's process
  • Replacing your team. The design target is autonomous handling of the routine cases and a better escalation for the rest, and we will say plainly which cases should never be autonomous
  • Regulatory sign off. We build the evidence trail your compliance function needs, and they sign it, not us
  • Ongoing operation of the workflow after handover, which is the Embedded AI Pod

How the work runs

S1

Scoping call and written brief

One call with the people who know the problem, then a written brief back to you within two working days: what we understood, what we think the hard part is, what is explicitly out of scope, and which of the four engagements fits. If none of them fits we say that instead of reshaping your problem to match a price list.

Output: A written brief and a named engagement, or an honest no
S2

Evaluation before implementation

Before any prompt is written we build a labelled set from your real examples with your domain experts, split by category, with a held back portion. The pass threshold goes in writing, with the consequence of missing it agreed at the same time. This is the week that makes every later argument about quality a number rather than an opinion.

Output: A labelled evaluation set, a harness, and an agreed threshold
S3

A thin slice, deployed

One path through the system, end to end, running in your cloud account, usually inside the second week. Not a prototype on a laptop. Deploying early is how integration risk, credential problems and data access surprises surface while there is still time to change the plan.

Output: A deployed slice on your infrastructure and a working pipeline
S4

Weekly build, demonstrated

Every week ends with a working demonstration against the evaluation set rather than a status document. You see the score, the failures, and what we are doing about them. Anything at risk is raised in the week it becomes at risk, not in the week it becomes a problem.

Output: Shipped increments, a score per week, and a visible risk list
S5

Hardening

Guardrails on the output path, escalation payloads, tracing, cost and latency instrumentation, alerts wired to people who can act, and runbooks for the two or three ways this specific system fails. Where the system can act, it shadow runs against live traffic before it is granted authority.

Output: Guardrails, dashboards, alerts, runbooks and shadow run results
S6

Handover, and then out

Your engineers make the last change while we are still there to watch. Repository, infrastructure as code, evaluation suite, documentation and access all sit with you, and none of it depends on an account we control. If you want us to stay, that is an Embedded AI Pod with its own scope, not a dependency we engineered into the build.

Output: Code, IP, docs, evaluation coverage and a team that can run it

Questions we get asked

What is the go or no go at the end of design?

The design phase ends with the process specification, the authority model, the connector plan and a build estimate against them. If any of those says the workflow is not ready to be automated, you stop there having paid for the design phase, and you keep the specification. That is a better outcome than a build that has to be switched off in month six.

How does the system prove why it did something?

Every run writes an ordered record: the inputs it read, the retrieval results it used with their versions, the step by step state, the confidence at each decision point, the action taken, and any human override. It exports as one record per case. This is the part that takes the longest to build and the part that decides whether the system survives its first serious question.

What happens when a source system changes?

Connectors validate their contracts and fail loudly rather than silently writing wrong data. Contract breaks page a human and pause the affected step rather than the whole workflow. That behaviour is designed and tested during the build, not discovered afterwards.

Can it run entirely inside our network?

It can run inside your cloud tenancy with data never leaving your perimeter, which is how comparable work has been delivered before. Fully air gapped changes the model options substantially, so raise it in the first conversation and we will tell you what is and is not possible rather than finding out in week nine.

This is the right one if

  • The process spans three or more systems and someone maintains a spreadsheet to hold it together
  • Rules based automation has been tried and it broke on the exceptions, which are most of the volume
  • An auditor, a regulator or a customer will eventually ask why a specific decision was made

Capabilities it leans on

  • Agent orchestration
  • Guardrails and escalation
  • Evaluation and quality gates
  • Observability, cost and drift
Start a build

$engineering / the other three

If this is not the shape you need.

01AI MVP SprintOne AI product idea taken from a blank repository to something real users can use, in six weeks, on your cloud account.$25k to $40k · 6 weeks02AI Integration PodAI features built into a product that already has customers, without destabilising the thing they are already paying for.$60k to $120k · 8 to 12 weeks04Embedded AI PodA standing engineering capability inside your team, for the year after the build when the interesting problems start.$18k to $32k per month · Three month minimum, then rolling

Bring the task, the data and what has already been tried.

You get a written view of the problem within one working day, from an engineer, including the version where the answer is that this is not a build.

Start a build