Why ForgeStack?

Use the tools you already have. Close the gaps they leave behind.

Regulated engineering teams rarely suffer from a lack of tools.

They already have CI/CD platforms, cloud services, governance systems, AI gateways, evaluation frameworks and monitoring.

The harder problem is turning those individual capabilities into a technical outcome that can be tested, evidenced and relied upon.

ForgeStack works across the systems you already use to help prove what happened, test what would happen under change or failure, and engineer the gaps exposed by that evidence.

Where ForgeStack fits

Different tool categories solve different parts of the problem.

ForgeStack is designed to work across them rather than replace them.

Legend

Core — typically central to the capability
Partial — may support part of the capability or require additional implementation
Not primary — not usually the main purpose of the tool or service

Where ForgeStack fits, by tool category
Capability	ForgeStack	CI/CD & cloud-native tools	GRC & compliance platforms	AI gateways & routing tools	AI evaluation & testing tools	General engineering consultancy
Reconstruct what happened during a production release	Core	Partial	Partial	Not primary	Not primary	Partial
Produce a consistent technical release record	Core	Partial	Partial	Not primary	Not primary	Partial
Identify missing or ambiguous release evidence	Core	Partial	Core	Not primary	Not primary	Partial
Automate evidence collection across engineering systems	Core	Partial	Core	Not primary	Not primary	Partial
Engineer release controls into CI/CD	Core	Core	Partial	Not primary	Not primary	Core
Define approval, exception and block behaviour around releases	Core	Partial	Partial	Not primary	Not primary	Core
Test a production AI workload against an alternative provider or model	Core	Not primary	Not primary	Partial	Core	Core
Test tool calls, structured outputs, retrieval and behaviour during AI switching	Core	Not primary	Not primary	Partial	Core	Core
Produce a dated technical AI exit record	Core	Not primary	Partial	Not primary	Partial	Partial
Remediate portability failures identified during testing	Core	Not primary	Not primary	Partial	Partial	Core
Compare AI cost against an explicit quality baseline	Core	Partial	Not primary	Partial	Core	Core
Engineer caching, routing, batching and model right-sizing	Core	Partial	Not primary	Core	Partial	Core
Build a customer-controlled alternative AI path	Core	Core	Not primary	Partial	Partial	Core
Engineer runtime controls around approved models, routing and fallback	Core	Partial	Partial	Core	Partial	Core
Connect technical controls to the organisation's actual policy	Core	Partial	Core	Partial	Partial	Core
Work across the customer's existing engineering stack	Core	Partial	Partial	Partial	Partial	Core
Deliver changes inside the customer's own environment	Core	Not primary	Not primary	Not primary	Not primary	Core
Start without requiring a new platform purchase	Core	Core	Partial	Partial	Partial	Core

Reconstruct what happened during a production release
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Partial
AI gateways & routing tools
Not primary
AI evaluation & testing tools
Not primary
General engineering consultancy
Partial
Produce a consistent technical release record
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Partial
AI gateways & routing tools
Not primary
AI evaluation & testing tools
Not primary
General engineering consultancy
Partial
Identify missing or ambiguous release evidence
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Core
AI gateways & routing tools
Not primary
AI evaluation & testing tools
Not primary
General engineering consultancy
Partial
Automate evidence collection across engineering systems
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Core
AI gateways & routing tools
Not primary
AI evaluation & testing tools
Not primary
General engineering consultancy
Partial
Engineer release controls into CI/CD
ForgeStack
Core
CI/CD & cloud-native tools
Core
GRC & compliance platforms
Partial
AI gateways & routing tools
Not primary
AI evaluation & testing tools
Not primary
General engineering consultancy
Core
Define approval, exception and block behaviour around releases
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Partial
AI gateways & routing tools
Not primary
AI evaluation & testing tools
Not primary
General engineering consultancy
Core
Test a production AI workload against an alternative provider or model
ForgeStack
Core
CI/CD & cloud-native tools
Not primary
GRC & compliance platforms
Not primary
AI gateways & routing tools
Partial
AI evaluation & testing tools
Core
General engineering consultancy
Core
Test tool calls, structured outputs, retrieval and behaviour during AI switching
ForgeStack
Core
CI/CD & cloud-native tools
Not primary
GRC & compliance platforms
Not primary
AI gateways & routing tools
Partial
AI evaluation & testing tools
Core
General engineering consultancy
Core
Produce a dated technical AI exit record
ForgeStack
Core
CI/CD & cloud-native tools
Not primary
GRC & compliance platforms
Partial
AI gateways & routing tools
Not primary
AI evaluation & testing tools
Partial
General engineering consultancy
Partial
Remediate portability failures identified during testing
ForgeStack
Core
CI/CD & cloud-native tools
Not primary
GRC & compliance platforms
Not primary
AI gateways & routing tools
Partial
AI evaluation & testing tools
Partial
General engineering consultancy
Core
Compare AI cost against an explicit quality baseline
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Not primary
AI gateways & routing tools
Partial
AI evaluation & testing tools
Core
General engineering consultancy
Core
Engineer caching, routing, batching and model right-sizing
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Not primary
AI gateways & routing tools
Core
AI evaluation & testing tools
Partial
General engineering consultancy
Core
Build a customer-controlled alternative AI path
ForgeStack
Core
CI/CD & cloud-native tools
Core
GRC & compliance platforms
Not primary
AI gateways & routing tools
Partial
AI evaluation & testing tools
Partial
General engineering consultancy
Core
Engineer runtime controls around approved models, routing and fallback
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Partial
AI gateways & routing tools
Core
AI evaluation & testing tools
Partial
General engineering consultancy
Core
Connect technical controls to the organisation's actual policy
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Core
AI gateways & routing tools
Partial
AI evaluation & testing tools
Partial
General engineering consultancy
Core
Work across the customer's existing engineering stack
ForgeStack
Core
CI/CD & cloud-native tools
Partial
GRC & compliance platforms
Partial
AI gateways & routing tools
Partial
AI evaluation & testing tools
Partial
General engineering consultancy
Core
Deliver changes inside the customer's own environment
ForgeStack
Core
CI/CD & cloud-native tools
Not primary
GRC & compliance platforms
Not primary
AI gateways & routing tools
Not primary
AI evaluation & testing tools
Not primary
General engineering consultancy
Core
Start without requiring a new platform purchase
ForgeStack
Core
CI/CD & cloud-native tools
Core
GRC & compliance platforms
Partial
AI gateways & routing tools
Partial
AI evaluation & testing tools
Partial
General engineering consultancy
Core

The difference is the engineering outcome

A CI/CD platform can enforce a deployment gate.

A GRC platform can collect control evidence.

An AI gateway can route between models.

An evaluation framework can compare model behaviour.

Those capabilities are valuable, and ForgeStack may use or recommend them where they are the right fit.

The gap is often what sits between those tools:

deciding what must actually be proved;
testing the real workflow rather than an isolated component;
joining records across systems;
defining what constitutes an acceptable exception;
fixing failures exposed by the test;
and leaving behind a technical record that can be reviewed later.

That is the layer ForgeStack provides.

Two primary engagements

Release Evidence

Prove what shipped.

ForgeStack turns production delivery records into a consistent technical evidence trail for each release.

We work with the information already produced by your engineering systems — pull requests, approvals, tests, security checks, change references and deployment records — and identify what is present, missing or ambiguous.

Start with: a Release Evidence engagement.

Where it can lead: Evidence Automation or Release Control Engineering where recurring gaps justify engineering work.

AI Provider Exit Drill

Prove your AI can switch.

ForgeStack tests whether a real AI workload continues to perform its business function when its current model or provider arrangement changes.

The drill records differences across tool calls, structured outputs, retrieval, permissions, behaviour, latency and cost, and leaves a dated technical record of what worked, what failed and what requires remediation.

Start with: an AI Provider Exit Drill.

Where it can lead: AI Exit Remediation, AI Model & API Cost Optimisation, Customer-Controlled AI Fallback or AI Runtime Control Engineering where the drill exposes a material gap.

Engineering where the evidence points

ForgeStack does not begin by selling a large transformation programme.

The first engagement establishes what is actually happening.

Further engineering is scoped only where the evidence supports it.

Evidence Automation
Reduce repeated reconstruction by capturing and linking release evidence consistently across engineering systems.
Release Control Engineering
Turn agreed release policy into technical controls around approvals, checks, exceptions and deployment paths.
AI Exit Remediation
Fix the prompts, tool calls, schemas, retrieval, permissions or degraded-mode behaviour that prevent credible provider switching.
AI Model & API Cost Optimisation
Reduce AI operating cost while testing changes against the workload's required behaviour and quality baseline.
Customer-Controlled AI Fallback
Build and test an alternative AI path using infrastructure, models and controls under the customer's ownership or control.
AI Runtime Control Engineering
Engineer rules around approved models, routing, budgets, fallbacks, schemas, exceptions and runtime decision records.

We do not need to replace your stack

Most teams already have useful engineering and governance tooling.

ForgeStack is not designed to force another platform into the environment.

Where an existing product already solves part of the problem well, we use it.

Where configuration is enough, we configure it.

Where custom engineering is required, we build it.

The objective is not to maximise the number of ForgeStack components.

The objective is to leave the customer with a technical process that works, can be tested and can be evidenced.

What ForgeStack does not claim

ForgeStack does not certify compliance.

We do not replace auditors, legal advisers or GRC platforms.

We do not claim that existing engineering or AI tools are incapable of solving parts of these problems.

Our role is to connect those capabilities to the customer's real delivery process or AI workload, engineer the gaps and leave the technical evidence behind.

Talk to ForgeStack

If you already know where the problem is, tell us what you are trying to prove, fix or control.

If you do not, start with one of the two primary engagements.

Release Evidence
Prove what happened during a production release.
AI Provider Exit Drill
Prove whether a production AI workload can move away from its current provider arrangement.

Talk to ForgeStack

Where ForgeStack fits

Reconstruct what happened during a production release

Produce a consistent technical release record

Identify missing or ambiguous release evidence

Automate evidence collection across engineering systems

Engineer release controls into CI/CD

Define approval, exception and block behaviour around releases

Test a production AI workload against an alternative provider or model

Test tool calls, structured outputs, retrieval and behaviour during AI switching

Produce a dated technical AI exit record

Remediate portability failures identified during testing

Compare AI cost against an explicit quality baseline

Engineer caching, routing, batching and model right-sizing

Build a customer-controlled alternative AI path

Engineer runtime controls around approved models, routing and fallback

Connect technical controls to the organisation's actual policy

Work across the customer's existing engineering stack

Deliver changes inside the customer's own environment

Start without requiring a new platform purchase

The difference is the engineering outcome

Two primary engagements

Engineering where the evidence points

We do not need to replace your stack

What ForgeStack does not claim

Talk to ForgeStack