AI that knows your project. From the first prompt.
Without ai-boost, what the AI knows depends on instruction files that someone has to keep updating by hand. With ai-boost, every AI agent on the team gets exactly the context the task needs, plus a standard that applies and is enforced. Management and development work from the same knowledge. Measurable in cost and quality.
What happens when AI agents join the team
Every session starts from zero. The AI knows neither the code nor the conventions, neither the tickets nor the decisions. New team members are no different: the knowledge sits in people's heads and old tickets, and onboarding drags on.
Instruction files like CLAUDE.md grow, and quality drops. Every session loads every instruction file in full, whatever the task. The context window fills up with rules that don't apply right now, and the answers get worse.
Quality depends on prompting skill. The senior gets usable code, the junior gets generic code, and both end up in the same repository.
Everyone builds their own setup. Tools, access and instruction files are set up by hand and passed on to colleagues. Nobody knows whether they are used.
Costs get out of hand. Tokens are billed per head. Nobody sees what each session cost, whether the rules applied, or whether the effort paid off.
Every team that works with AI needs a solution for this. Building it yourself takes far more effort than expected, and the field keeps moving at ever shorter intervals.
Context Management
The AI knows the project before it writes the first line.
Code, conventions and commit history are semantically searchable. Tickets and documentation are connected and linked to the code. With every prompt, ai-boost supplies the right context automatically, without the AI having to ask for it. After every search, a model checks whether the results actually answer the question.
- Ticket system and wiki connected once, centrally, e.g. Jira and Confluence, always up to date
- Every ticket shows the commits that implemented it
- False hits are discarded, a failed search is reported openly
Token Management
You see what it costs and what it delivers.
Usage, cost and models are recorded per session and attributed to developer, repository and project. The Admin Portal shows metrics with trends and drill-down to the individual session. Controlled experiments show whether a feature improves cost and quality before it applies to everyone.
- Cost per prompt, developer and project
- Verdict from experiments: better, worse, no difference
- Parallel agents only with explicit approval
Quality Management
The same quality, no matter who writes the prompt.
Every session with code changes rates itself against four criteria, and an independent AI reviewer rates the code based on the actual changes. Sessions that do well in both ratings become templates for later tasks of the same type. Sessions that went badly become lessons.
- Four criteria: rule adherence, completeness, architectural fit, context value
- LLM-as-a-Judge: an independent second opinion on the diff, blind to the self-assessment
- Lessons from earlier sessions are delivered with the prompt
Workflow Management
One standard for everyone, and it applies.
Tools, integrations, models per phase, mandatory steps and working rules are defined once, centrally, in the portal and distributed to every machine. No developer has to set anything up. In the session, deviations from the specified model are rejected, and a pending review pauses the session. Every version is traceable.
- One endpoint per project instead of many tools per developer
- Plan, Delegate, Review with a fixed model for each role
- Human-in-the-loop configured centrally: pause after the plan, approval for each write access, rule check before publishing
Planning and development work from the same knowledge.
Anyone who plans, prioritizes and talks to customers connects to the project from Claude Desktop with their company account. No installation, no key. The same sources as the developers, access to the project documentation and six ready-made skills.
Ask the product. How does something work today, why is it that way, and is something else possible. Every statement with a source, so new team members find their way around quickly too.
Find and summarize. Find tickets and pages by meaning, spot duplicates, summarize an epic or a discussion in a few lines.
Prepare a task. An idea or a customer email becomes a ticket that the team and AI agents can implement directly.
Is this a change? Check a request against the agreed scope and the code, including a draft reply.
Check status. What the ticket system says, compared with what the code shows. One version for the team, one for the customer.
Document. Record decisions, processes and results directly in the project documentation, with a preview before every write.
Ticket system and documentation are only written to after a preview and explicit approval. Decisions stay with people.
Seeing beats reading: in 45 minutes we show ai-boost live on a real project.
Request a callEverything included. Nothing to build, nothing to wire up.
One command per machine, one command per project. That is all the setup it takes: no servers of your own, no scripts, no prompt engineering in the team.
- Platform
- Hosted service with one endpoint per project, usable by any AI agent that supports the open standard. Developer Portal and Admin Portal.
- Knowledge
- Code and commit history, project management and documentation software, e.g. Jira and Confluence, always up to date, tickets linked to code.
- Tools
- Search across code, history, conventions and project knowledge. Create, comment on and edit tickets, set status, read and extend documentation, each with approval and confirmation.
- Control
- A central way of working and rules, distributed to every machine and enforced in every session. Context with every prompt.
- Measurement
- Rating of every session, independent reviewer, experiments, cost per developer and project.
- Operations
- German data center, personal keys, credential scrubbing, fixed deletion periods, updates without any effort on your side.
Onboarding and pricing
Ramp-up phase, three months.
Kick-off workshop, setup and connection of your sources, onboarding and training, support, acceptance. A dedicated contact from the AI team.
Price: on request, depending on team size and sources.
Software license, monthly, as a flat rate for named developers. Unlimited projects and repositories. Management portal included.
| Package | Developers | monthly | additional developer |
|---|---|---|---|
| Team | 10 | €899 | €79 |
| Business | 25 | €1,790 | €69 |
| Enterprise | 50 | €2,990 | €49 |
All prices plus VAT. Licenses for AI models and access to ticket system, wiki and version control are provided by the customer.
Let's talk about your team.
In 45 minutes we show ai-boost live on a real project. Afterwards you will know whether it fits.
Or directly: info@metadevs.de