For software teams that already build with AI and hit the same wall: the code is fast, the requirements are not ready for it.
GP Solutions sets up the process and the tooling that make requirements AI-ready. Documents get identifiers, cross-references and a top-down structure. A change starts at the top, and every affected requirement is found for you.
Your team keeps writing the code. We work on what happens before it.
Pick the one closest to yours. Each opens with what today looks like and what changes after the work.
Today: you bought the tools, the hardware and the training. The agents write code quickly. Delivery speed barely moved.
The bottleneck moved. A developer with fifteen years in your domain built a feature from a two-line ticket. An agent cannot. It needs the business need, the cases, the boundaries and the acceptance criteria written down. We put them there.
Today: you ask a developer how their task works. They did not write it, an agent did. The answer is a shrug.
The developer stays the owner of the decision, not of every line. Before an agent writes code, it writes a technical design. The developer reviews that document, corrects it, and only then starts the run.
Today: the project is live, the specifications are scattered across documents and chats, and there is nothing you can hand to developers or agents.
We rebuild the corpus from what you have: transcripts of meetings, existing documents, tickets. Each business need, requirement and user story gets an identifier and its links. What is missing becomes a list of questions, not a guess.
Today: ten people spend the first half of a meeting reconstructing why a decision was made three months ago, and sometimes what the decision was.
A decision log lives inside the corpus. Each decision records the reason and links to the requirements it changed. When the reason changes, the affected requirements are listed automatically.
Code has identifiers, references and tooling that shows what a change touches. We give your requirements the same properties, so the AI that already works well with code works well with them.
Meetings with the customer and inside the team are recorded and transcribed. The transcript is the input. Nobody goes into a document and edits it by hand first.
Business needs, requirements, user stories, acceptance criteria, decisions and open questions become items with identifiers and cross-references. Documents become a graph, not a folder.
A change starts at the vision or the business need. The harness walks down through the linked items and lists everything that contradicts, depends or must be updated.
Reviewers comment on any element. One click turns the comment into a prompt; the agent explains, elaborates or fixes, and records the decision next to the requirement.
Requirements are the first stage of a pipeline we run on our own product. Each stage has one owner. People decide, agents execute, and every hand-off is a reviewed document, not a chat.
The agent extracts business needs, requirements and user stories from transcripts, then reviews its own output: gaps, contradictions, mismatches with what the system already does.
From the specification, grooming notes and technical discussions the agent writes a short technical design: what will be built, where, and how.
Specialized sub-agents build backend, frontend and services in isolated contexts, then a verification agent checks the result against the design and the specification.
Testing agents decide what to test from the acceptance criteria, run the scenarios and report what broke. Deterministic checks are scripts, not prompts.
A documentation agent opens the application, walks through the feature, takes screenshots and updates the user manual. Technical design and implementation notes stay in the repository.
A new customer request lands as a transcript again. The impact analysis from stage one finds every item it touches, and the pipeline repeats only for those.
GP Solutions configures the harness on your corpus, trains your analysts and developers, and stays until the first changes run through it end to end.
Identifiers and cross-references on every item. The point is not that a person can click through; it is that an agent can walk the graph and check consistency.
Change the business need, get the list of affected user stories, criteria and decisions. Then decide, then edit.
Decisions, open questions and assumptions are items with identifiers. A reversed decision shows up in the impact list like any other change.
The tooling is a set of skills, agents, commands and hooks configured for your project structure. Everything is plain text. It runs on Claude Code and has been ported to another model harness.
Reviewers work inside the documents. A comment becomes a prompt, the agent answers or fixes, the record stays in place.
Independent review agents verify the work of the agents that produced it. Deterministic steps run as scripts. Agents run in sandboxed environments with no access to your production data unless you grant it.
Analysts spend their time on decisions instead of rewriting. Developers review designs and code instead of typing. We train both, on your project.
Discovery on your real corpus, a pilot on one product area with your team, then rollout and handover. You get the estimate after discovery, not before.
We do not write your code and we do not replace your vendors. We make the input to development good enough for people and agents alike.
Each part of the method has a name the industry already uses. What is new is running them together, with agents as the readers.
Requirements are one kind of structured document. Marketing plans, HR policies and project documentation grow the same way: many documents, written faster than anyone can remember them, drifting apart. The artifact types change; the identifiers, links and top-down change stay.
GP Travel Enterprise is a travel reservation platform we have built and sold for many years. Its requirements corpus is where the method was developed and where it runs every day.
A service with a ready toolset behind it. We bring the harness, the artifact types and the process from our own product and configure them on your corpus. You are not adapting to a product; the tooling adapts to how your documents and team work.
Our primary harness is Claude Code. The skills, agents, commands and rules are plain text, and we have ported the set to another model harness. If your company standardizes on a different assistant, tell us which one and we will say what the port involves.
Your developers, or your vendor. We work on requirements, technical design review and the hand-off. We do not take over development.
Documents need structure: types of artifacts, identifiers, links. If requirements today live in loose documents and chats, that changes. The process adapts to you, and you adapt part of your habits to the process. We say which part during discovery.
It changes their work. The agent does the first pass: extraction, gap checks, contradictions, questions. The analyst answers the questions, decides, and talks to the customer. Fewer rewrites, more decisions.
With a discovery on your real corpus, not a sample. We look at how requirements are written today, where they live, and how a change travels. You get a plan and an estimate after that.
Agents run in sandboxed environments with guardrails, on corporate subscriptions. They get no access to your production systems or customer data unless you decide otherwise for a specific task.
Three ways. Independent review agents check the output of the agents that produced it. Acceptance criteria are checked one by one against the specification and the system. Everything deterministic, such as formatting or document generation, runs as a script, not as a prompt.
Reads the specification, reviews the technical design the agent wrote, corrects it, starts the run, then reviews the code. The developer owns the decisions. The agent owns the typing.
It depends on the size of the corpus and how your team works today. Discovery gives you the estimate. A pilot on one product area comes before a rollout, so the first result arrives early.
No. The method was developed on a travel product, but it works on requirements as documents. Domain knowledge stays with your team; the structure and the tooling are domain-neutral.
A demo shows the harness on our own requirements. Bring a specification of yours and we will run a change through it.
GP Solutions GmbH, Unterschleißheim near Munich. Engineering hubs in Poland.
