pgraph checks every AI change against your code, your data and your team's decisions before merge. Deterministically, not by another model's opinion. And Rabotnik, the AI team that runs on the graph, cuts the token bill in half.
Teams adopt AI agents for speed. Within months the bottleneck moves to review, and the failures that reach production are the ones no test was written for.
The agent wires ui/ straight into db/ to make a feature work. 412 tests pass. The layering you spent years on erodes one PR at a time.
DataAI rewrites PL/SQL and generates migrations. It passes on staging. The quarterly report that reads the dropped column fails in production a week later.
KnowledgeA new session, a new model, a compacted context. The next PR brings back the approach the owner rejected last week, and someone has to catch it again.
RabotnikSeats on top of seats, one frontier model for every step, a monthly invoice with no breakdown. You cannot tell what a feature cost or where the tokens went.
You describe what you need in the chat or a ticket.
Spec, code and tests, on your models.
Code and data rules run on the change, deterministically.
A clean change with evidence, ready to merge.
A second model reviewing the first one shares its blind spots. A graph query gives the same answer every time and shows its reasoning as a path you can open.
AI work scales with tasks, not headcount. You pay for what the agents actually compute, on the provider or the hardware you choose.
A function calls a function. A service reads a table. A task changes a module. When everything lives in one graph, "what breaks if I change this?" is answered by a query, not by a guess.
Functions, modules, tables, columns, queries, tasks, pull requests.
Calls, imports, reads, writes, depends on, changed by.
Complexity, owner, size, type, sensitivity, last change, who approved it.
Your code, your data and what your team knows live in one property graph. Rabotnik, the AI team, works on top of it, and the graph checks every change it makes.
Closes: green tests, broken architecture
A deterministic graph of your code (GoCPG, CodeGraph). The engine computes the findings, not an LLM.
Closes: migrations that break what reads the data
Schemas, relations and lineage in one graph, native to Postgres. Built by the Postgres Pro team.
Closes: agents that forget what was decided
Decisions, tickets, people, commits and conversations, linked by typed edges. Before an agent acts, it asks the graph what was already decided.
Closes: invisible AI spend and the review bottleneck
Orchestrator, executors and reviewers that take a ticket to a reviewed change. Every change is checked against the graph before merge.
Tests pass. The architecture rule fails. That is the moment we exist for.
AI executors write requirements, plans, code, tests and docs. The code graph checks architecture, security and quality on every change. Management sees deadlines, dependencies, risks and release readiness across the whole portfolio, not per team.
Five managed stages: goal and acceptance criteria, plan with affected systems, execution by people and AI, automated checks, a release decision with visible risks.
Calls, imports, data access and ownership in one graph. The question "what does this change touch?" gets an answer with a path you can open, not an estimate.
Git, your tracker, CI/CD, test systems, documentation and the AI assistants your engineers already use. One more check in the pipeline, not a new platform to migrate to.
Scenario figures are pilot hypotheses, measured on your repositories before any commercial commitment.
Moving off Oracle, splitting a processing contour by geography, leaving Greenplum for Greengage or Cloudberry: the risk is not in the data volume, it is in the dependencies nobody has mapped. We put schemas, code, queries and data flows into one graph and check every step of the migration against it.
Which application code, procedures, reports and ETL jobs read and write each table and column. Dead objects, hidden coupling, the order in which contours can be cut over.
AI rewrites PL/SQL and queries and generates migrations. The graph checks them before rollout: type and semantic mismatches, broken lineage, replication and CDC paths that would lose data.
Every schema change shows its impact before it is made. Analytical agents work on top of the graph and bring back answers, not raw dumps, with a path to the source.
Banks and large enterprises are moving core contours to Postgres: terabytes of updates a day, analytics with no more than a day of lag, zero tolerance for downtime or data loss. These programs are budgeted in the hundreds of millions to billions of rubles, and most of the cost is risk and rework.
The team comes from Postgres Pro and Yandex Managed PostgreSQL and has taken part in large bank migrations to Postgres Pro first-hand. We know how these projects fail and where the graph saves weeks.
Early access: we are taking two or three design partners with an active migration program.
Rabotnik keeps what the team knows as a graph: tickets, decisions, people, commits, code and conversations, linked by typed edges. Before an agent proposes something, it asks the graph what was already decided, by whom and why.
Every decision is a node with an author, a date and the decision it replaces. An agent that is about to bring back something the team already rejected finds that out from a query, not from you.
Open a ticket and the agent gets what is linked to it: the spec, reviews, rulings, related commits and the conversations where it was discussed. Neighbours in the graph, not the top-k most similar paragraphs.
Tickets and decisions point to functions and modules in the Code Property Graph. "Why is this code like this?" leads to the ticket, the review and the chat where it was agreed.
Memory lives outside the model. Opus, GPT and a local Qwen read the same graph, and after a context compaction the agent restores the working set from it instead of starting over.
Only the orchestrator writes; executors and reviewers read. A rule is attributed: what the owner said in the chat is kept apart from what a model wrote into a ticket, so agents do not obey rules nobody gave.
Orchestrator, executors and reviewers take a ticket from spec to a reviewed change, in Slack, Mattermost or Telegram, connected to Jira, Confluence and your CI. The difference from a vendor IDE agent is the economics: every token is accounted for, budgeted and routed to the cheapest model that can do the step.
Every call is attributed to a ticket, a stage, a role and a model: input, output, cache reads and writes separately. You see what a feature cost, not just the monthly invoice.
Limits per team, project and task. Near the limit Rabotnik compacts the session, switches to a cheaper model or pauses, instead of failing mid-task. FinOps for inference.
Sessions are compacted and kept warm in the prompt cache, so a repeat costs a cache read. With the code graph the agent reads the nodes it needs instead of whole files.
A frontier model writes the spec and reviews; a cheaper or local model executes. A vendor harness keeps every step on its own models. Rabotnik is not tied to a vendor: Anthropic, OpenAI, open-weight models on your own GPUs, in one task.
AI work scales with tasks, not headcount. One inference budget for the team, on the provider or hardware you choose, inside your perimeter if needed.
2x cheaper than the cheapest single frontier model, 3.6x cheaper than Opus alone. Real ledger of one ticket from our own pipeline (T-724, October 2026), priced at public API list rates. Rabotnik: $6.36. The same tokens on Opus 5.5 alone: $22.96 (3.6x), on GPT-6 Sol alone: $12.38 (1.9x). The local Qwen runs on our own GPU and costs no API fee. A single frontier model may need fewer retries, so in the pilot we measure the same task both ways.
Rabotnik. Infrastructure and AI harnesses. Postgres Pro core team.
GoCPG and CodeGraph. Code analysis and code property graphs. Postgres Pro, Yandex Managed PostgreSQL.
Business and partnerships. Pilots and enterprise sales.
We run a pilot on your code and your inference and show what the graph finds that your tests do not.