Solutions
A governed line where AI agents build, test and ship software and your engineers steer.
01Overview
Coding assistants made individual developers faster. They did not, on their own, make organizations faster. Public research on AI-assisted development, including the DORA programme, finds that AI acts as an amplifier: teams with strong practices gain, while weak review, testing and release habits are exposed. When code arrives faster than it can be reviewed, tested and operated, the bottleneck moves and quality can slip.
An AI software factory is the answer to that shift. It is the full loop from a request to running software, with agents doing the planning, coding, testing and revising, and with the controls that keep the output safe: written specifications, shared context, isolated build environments, automated checks, human approval at the right points, and monitoring that feeds back into the next piece of work. Coding agents are parts of the factory. The factory is the line around them.
We design and stand up that line with your engineering organization. We start with the software you need to produce and the rules it must meet, then build the context, pipelines, guardrails and operating model to match. Your engineers move from writing every change to setting intent, tuning the system and reviewing evidence. We do not promise that agents replace engineers. We help you decide how much autonomy each kind of work has earned.
02
The terms sound alike and mean different things. An AI factory, in the sense used on our AI Factories page, is the compute, data and process that build and ship models and agents. An AI software factory uses agents to build ordinary software: applications, services, integrations and internal tools.
The idea itself is old. Software factories were proposed in the 1960s and revisited in the 2000s as a way to build software from repeatable patterns. What is new is that agents can now do a large share of the work inside the line. Many organizations need both: a factory for intelligence and a factory for the software around it.
03What you get
We set up how work enters the line: requests are triaged, turned into written specifications with acceptance criteria, and linked to the business outcome they serve. Specifications are readable by people and by agents, and they become the contract that tests and reviews check against.
Agents build what they are told, so the instruction is the product.
Agents are only as good as what they know. We organise your repositories, architecture decisions, coding standards, domain definitions and runbooks into context agents can use, with permissions that limit what each agent can see. Standards are written as checks, not only as documents.
Good context is the difference between plausible code and correct code.
We design the roles and hand-offs: planners, builders, testers, reviewers and release agents, each with a narrow task, clear inputs and a defined output. Work runs in parallel where it is independent and stops where a person must decide. We select models and tools by task and cost, and keep them swappable.
A defined process turns agent output from craft into production.
Agents with credentials and network access are a security surface. We run agents in isolated environments with scoped, short-lived credentials, restricted network egress and logged tool use. Secrets never live in prompts or agent definitions, and access follows the same identity rules as people.
An agent should never hold more access than the task needs.
Every change meets automated tests, static analysis, dependency and secret scanning, and review before it can merge. We tier review by risk: automated gates for routine changes, named human approval for sensitive paths such as payments, identity and data access. Releases roll out in stages and can be rolled back.
Review capacity, not typing speed, limits safe throughput.
We measure the line itself: lead time, change failure rate, time to recover, review wait, defects that escaped and the cost of agent usage per change. Budgets and alerts stop runaway spend, and operational reviews on a fixed cadence decide where agents have earned more autonomy and where they have not.
Speed that is not measured against stability is a guess.
Free 30 minutes with an engineer.
04African sectors
Situation
A group has a long backlog of integrations and internal tools, and strict change control that makes every release slow.
What it does
A factory produces changes with specifications, tests and evidence attached, so change boards review proof rather than start from scratch.
Human decides
Engineering and risk leaders set which change classes agents may prepare and who approves each class.
05Boltcliq platform
Boltcliq product
Provides isolated environments, sandboxes, storage and secrets for agents and pipelines, portable across providers.
A safe place for agents to work.
Boltcliq product
Governs agent context, memory and permissions, and holds the standards and approval rules that changes must meet.
Governance is part of the line.
Boltcliq product
Tracks delivery, quality, incidents and agent cost across every team and release.
Leaders see what the factory delivers.
06
Does
Does not
07FAQ
Experience you can lean on
Immersed in technology and across many different verticals, so the people on your project have done this before.
AI Software Factory
Tell us the problem. An engineer, not a salesperson, answers.
A governed line where AI agents build, test and ship software and your engineers steer.
