One AI in front, a team behind it.
You talk to Gravibot the way you would talk to the engineer leading a project. You do not manage the agents behind it.
The discovery agent runs interviews, reads documents, and learns from recordings of the work. The architecture agent models the business and designs the new workflow. The build agent writes code and connects systems. The verification agent tests the result and sends failures back for repair. The deployment agent releases the work under the agreed access. The evolution agent watches production and starts the next round.
Each agent hands the next one something it can use directly, such as a confirmed rule, a design with acceptance criteria, or a failing test with its cause. That handoff is the hard part of delivery, and Gravibot does it without a person in between.
A model of your business.
Gravibot builds a model of your processes, business objects, systems, and the people responsible for them. Interviews, documents, recordings, and system records each add evidence.
A business rule keeps its source, its scope, and its owner. A disagreement stays an open question until the owner decides. Gravibot traces each requirement into a workflow, a piece of code, and a test.
When an approval threshold changes, Gravibot finds every decision and system it touches. When a new workflow needs the same customer records, Gravibot checks the existing integration before it builds another one.
It builds whatever the work needs.
Gravibot picks the form of each part of the solution. An agent reads inputs that vary. A program applies a fixed rule. An integration updates an existing application. A review screen lets people handle exceptions.
It configures the applications you already have and builds only what is missing. Every component has one job, a link to the requirement it serves, and a test that checks it.
The work is visible as it happens. You see the goal, the plan, the files and configuration changes, and the decisions waiting on you.
Tests it did not write for itself.
Code that passes its own tests proves little. Gravibot checks behavior against acceptance criteria and reference cases your team confirmed before the build started.
Tests look at data, decisions, permissions, and the final state across connected applications. A failure stays on record with its evidence. Gravibot finds the cause, fixes the code or raises a question about the requirement, and reruns the tests.
Every release carries its test results and its known limits, so your team knows what the system handles alone and where it needs a person.
It stays after launch.
Gravibot keeps watching the systems it shipped. It tracks the outcomes you care about, investigates exceptions, and spots changes that affect the implementation.
A policy update produces a rule change and new tests. A changed interface produces an integration repair. An unexpected case produces a question for the process owner and a proposed update.
Every change goes through the same tests and release rules as the first build. An incident becomes a test and a recovery procedure, so the next version handles it.
Delivery memory.
Delivery memory is the full record of each system, from the first interview to its current version in production. It holds five kinds of record, all linked to each other:
- Business context. Objects, systems, processes, decision rules, and where each came from.
- Delivery spec. The goal, the target workflow, the acceptance criteria, and the open questions.
- Implementation state. Code, configuration, connectors, access, versions, and what is deployed where.
- Verification history. Every test, failure, fix, and the evidence used to accept a release.
- Operational learning. Production results, business feedback, changes, and lessons that apply elsewhere.
Three months after launch, your purchasing team starts allowing partial receipts. Gravibot already knows the purchase objects, the approval rules, the code, the tests, and the systems involved. It proposes the change, updates the code, reruns the tests, and releases within its authority, all without repeating the interviews.
Inside your company, this record makes each new workflow faster to build. Across customers, Gravibot reuses engineering methods and tests, and your data and private rules never leave your company's record.