AI Enablement for Engineering

Human and agent pods that ship faster, with every change reviewed.

Twistag brings its own way of working into the client’s engineering team: specs before code, delivery agents across the lifecycle, an agentic review layer on every pull request and one dashboard for engineering health. Senior engineers keep the final call.

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Rows of white server cabinets under hanging light rails

Why enablement

AI writes code faster than teams can review it. The gain only holds when the way of working changes with it.

01Every engineer using AI differently. Different tools, prompts and standards in every squad.
02More pull requests, same review capacity. Quality drops as volume rises.
03No numbers leadership can defend. Adoption is visible; impact is not.

What changes

The delivery system Twistag runs on, set up inside the client’s team.

Twistag runs on this system itself: about 90% of the code it ships is spec-driven and AI-generated, and engineers focus their judgement on the rest.

01BaselineCycle time, throughput, coverage and defects before anything changes.
02Spec-first workflowWritten specs with acceptance criteria before any code.
03Delivery agentsAgents for specs, architecture, code, quality and deployment, in the existing toolchain.
04Agentic review layerChecks on every pull request; a failure halts the merge until it is fixed or overridden.
05Human + agent podsSmall senior teams, each engineer working with agents, with people on every decision.
06Engineering dashboardQuality, security, cost, adoption and delivery in one view.

ExampleWeek 1 vs week 12

Measured against the baseline

Cycle time~~Baseline 3.4 days~~ after 12 weeks 1.2 days

Pull requests merged per week~~Baseline 42~~ after 12 weeks 95

Test coverage~~Baseline 71%~~ after 12 weeks 90%

Defects found after release~~Baseline 31~~ after 12 weeks 17

Example figuresSame team, same roadmap

How it runs

Eight to sixteen weeks, set by the size of the team.

Pods start on live roadmap work by the middle of the programme, and the results are measured against the baseline at the end.

Engineers in scope

8 weeks

Baseline and assessmentCodebases, tooling and bottlenecks

Environment and guardrailsAccess, policies and audit trail

Spec-first workflow and agentsTemplates, agents, coaching

Review layer and dashboardChecks on every pull request

Pods on roadmap workA real product item, shipped

Results reviewMeasured against the baseline

An engineering lead as sponsorOwns the change and the targets.

Access to repos, CI and backlogIn the company’s own accounts.

A real roadmap itemThe pods prove the model on live work.

After the programme

Build more, govern it or run it alone.

Custom Software DevelopmentBuild the next product

The same pods take on the roadmap, from MVP to platform.

Next step Agent Governance & SecurityGovern the agents

Permissions, audit trails and evaluations for every agent in the delivery chain.

Next step

Full handoverRun it in-house

Spec templates, review checks and the dashboard handed over to the engineering team.

Questions

About AI enablement for engineering.

Which AI coding tools does it work with?

Any of the main assistants and models. The set-up runs inside the IDE, Git, CI and backlog the team already uses, and the tools can change without changing the process.

Does it replace engineers?

No. Senior engineers own every decision. Agents take the volume, so people can spend their time on architecture, hard problems and the product.

What is Pulse?

Pulse is Twistag’s own delivery methodology: five delivery agents, an eight-check review layer and one dashboard. The programme sets up the same model inside the client’s team.

How is code and IP protected?

Code stays in the company’s repositories and cloud. Access tiers, policies and an audit trail are set up before any agent runs.

How is the impact measured?

Cycle time, pull request throughput, test coverage, defect escape and developer satisfaction, against the baseline taken at the start.

What does it cost?

Cost follows scope: the number of engineers, codebases and teams involved. A proposal follows within a week of the first call.

Other Product Engineering services.

Product Engineering overview

AI-Native Product StrategyFrom problem to a validated productExplore Custom Software DevelopmentFrom MVP to platforms built to carry AIExplore Agentic Platforms & Operation CentresThe product around a company’s agentsExplore

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