Ship faster,
document
automatically
A native end-to-end AI platform for building IT products: documents are generated automatically at every stage, from requirements to production.
Your team delivers several times more, and documentation stops being manual work.
- Documentation −70%
- Spec detailing −50%
- Development −70%
- Throughput ×2
Speed is capped by people, documentation by manual work
Hiring keeps getting more expensive, key engineers are overloaded, and documentation requirements only grow. Point AI assistants speed up code generation, but they do not change the process and do not solve the documentation problem.
Not another assistant, but a platform for the whole cycle
Three components that work together: a structured process, AI agents for every stage, and automatic documentation built into the flow of work.
Platform: from requirement to production
A structured SDLC path with control points. It is configured around your processes and standards, not the other way around. Adaptation to industry and internal regulations, with a human in the loop on every decision.
AI agents: one agent per stage
AI agents prepare drafts, routine work, and traceability; the engineer reviews and approves. Requirements, design, code, tests — acceleration without losing quality control.
Auto-documentation: the document comes with the artefact
Documentation is produced as the work goes, not written by hand after delivery. It is always ready for acceptance: specifications, design documents, test cases, and traceability from requirements to code and tests.
Six stages — and each one produces a finished document
The document is born together with the stage: every step ends with a ready artefact, not a task to write it up later.
Requirements: analysis, clarification, acceptance criteria → specification and criteria
Design: interfaces and user scenarios → mockups and UX description
Architecture: solution design derived from context → design document
Development: code generation and review supervised by an engineer → code and traceability
Testing: test cases and data straight from the requirements → test cases
Acceptance: a document set in your own standard → complete package
A process you can actually see on screen
Steps are named by action rather than jargon, the order is up to the developer, the repository, branch, and provider are visible at a glance, and the work runs locally.
Business Benefits
Documentation effort: −70%
- Plan vs actual on a pilot rollout
- A team of 4 developers
- Company profile: 10,000+ employees, 260 of them in IT
Requirements detailing: −50%
- Requirements and design are no longer the bottleneck
- Early stages stopped pushing deadlines
Development: −70%
- Time removed from development against the plan
- Through code generation and review supervised by an engineer
Throughput: ×2
- Figures from one specific rollout
- Your effect depends on your processes and industry — we calculate it on your data
Do not take our word for it — calculate the effect yourself
Enter your team size, salaries, and the share of time spent on documentation — the calculator shows ROI, payback period, and infrastructure cost for the deployment option you choose: cloud, private cloud, or on-premise.
For illustration: a team of 20, a 45% productivity gain, a realization factor of ×0.6, cloud deployment — roughly 95% ROI per year. Exact numbers come from the calculator, on your data.
- Development team — size and structure
- Productivity gain
- Effect realization factor
- Deployment format and infrastructure cost
- Estimated annual ROI and payback period
Pricing Options
Choose the optimal way to work with the platform
Cloud
OpEx cost model, billed monthly. The lowest entry barrier — you start with almost no upfront investment.
- Lowest entry barrier, start with almost no investment
- Code is processed on the AI vendor's infrastructure — outside your perimeter
- Best suited for a pilot and mid-sized teams
Production data is never involved in the process, in any of the options.
Private cloud
OpEx cost model, billed monthly. The best balance between cost and code sovereignty.
- Secured cloud provider infrastructure with a contractual guarantee
- Code and data never leave the perimeter
- Top-tier open models
- The best cost-to-code-sovereignty ratio
The choice of format only affects where code is processed during development.
On-premise
CapEx cost model, with depreciation. For regulated industries and strict information security requirements.
- Models run inside your perimeter — code never leaves it
- For regulated industries and strict security requirements
- Economically sound at scale
All three formats are supported by the platform and covered by the calculator.
Test it on one team in 4–6 weeks
Before rolling the platform out across all of development, measure the effect on a real project of yours.
Paid diagnostic pilot
- Duration — 4 to 6 weeks
- Fixed price
- Success criteria agreed up front
Effect measurement
- Plan vs actual on your project
- Metrics agreed before the start
- A report with figures once the pilot ends
Moving to rollout
- The pilot fee counts towards the rollout
- The platform is extended to your other teams
- Templates adapted to your standards
Frequently asked questions
How is this different from Copilot and other AI assistants?
An assistant helps you type code faster. An AI development platform runs the entire development cycle following a purpose-built methodology: a structured process, AI agents at every stage, integrations with your systems, and documentation produced alongside the work. We remove manual work exactly where teams lose the most time.
Will our data leave our perimeter?
An important distinction: production data is not involved in the process at all — it is created and stored only in the production environment inside the client perimeter and never leaves it, whichever option you pick. The choice of format concerns code only, specifically where it is processed during development. With on-premise deployment the models run inside your perimeter and code never leaves it. In a private cloud the model runs on secured cloud provider infrastructure with a contractual guarantee, so code stays inside the perimeter as well. In the cloud, code is processed on the AI vendor infrastructure, outside your perimeter: an option for a fast start and for codebases without strict protection requirements.
Can we trust documents prepared by AI?
The human stays in the loop. AI prepares drafts and routine work — formatting, traceability, test cases — while the engineer reviews and approves. During the pilot you judge the quality yourself, on your own project.
We have our own documentation standards. Do we have to change everything?
No. The platform is configured around your templates and standards, including industry requirements and internal regulations. Adapting documents to your format is part of the pilot.
How can we estimate the effect before rollout?
In two steps: the calculator gives an ROI forecast based on your numbers, and a paid pilot on one team turns that forecast into fact, with plan vs actual measured over 4–6 weeks.
Request a pilot rollout or a calculation for your team
Leave your contacts and we will come back with an offer for your situation and an ROI calculation on your data. No commitments: a personal effect estimate, terms for a paid rollout on your project, and answers on security and deployment.
If you have any questions, contact our manager: sales@it-balance.uz