Grok for your own product .
Ask anything about your code, infra, tickets, and tests — with citations. Self-hosted, so the answers and the sources stay on your servers.
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@Oracus how does our abandoned cart recovery work?
Ask anything
How a feature works and which services it touches, what depends on what, bug coverage, release status, who-owns-what — if it lives in something Oracus.ai has ingested, you can ask it. Every answer cites the ticket, test, file, or service it came from.
› How does checkout work, and which services are involved?
frontend calls checkoutservice, which orchestrates cartservice, productcatalogservice, currencyservice, shippingservice, paymentservice, and emailservice. The order flow and pricing rules are documented in SHOP-50.
› What depends on productcatalogservice?
It's called by frontend, recommendationservice, and checkoutservice — derived from the Kubernetes manifests. Checkout can't price a cart without it, so a change there ripples into the order flow.
› Is the cart-cleared-on-retry bug (SHOP-203) covered by a test?
Yes — it's referenced by the retry test [shop.checkout.retry], and the scenario is documented in SHOP-50. The test and the ticket are linked.
› Who shipped the rate limiter, and where's the spec?
PLAT-91 (closed 2026-02-14, owned by @ana). Spec lives in `specs/rate-limit.md`; tests in `apps/api/tests/rate-limit.test.ts`.
› Are we behind on the Q3 mobile epic?
14 stories in MOBILE-Q3: 9 Done, 3 In Progress, 2 Backlog. The 3 in-flight tickets all block on PLAT-118.
› Which services would a currency change touch?
currencyservice owns conversion and is called by both frontend and checkoutservice, so cart totals and the final order total both depend on it. Behavior is specified in SHOP-72.
Map your architecture, not your guesses
Oracus.ai reads what's actually deployed — Kubernetes manifests,
Dockerfiles, .proto packages, OpenAPI specs, and
package manifests — to derive a cross-repo service map directly
from infrastructure-as-code. No diagrams to maintain, no whiteboard
kept up to date by hand. The map below is generated from a real
Oracus.ai ingest of a public reference architecture.
Covers REST, gRPC, queues, and shared libraries — refreshed on every ingest, so the map is never the stale one on someone's laptop.
Sources flow in, cited answers flow out
One grounded answer surface across every system your team treats as source-of-truth. Tickets, tests, specs, and docs — connected, embedded, and citing the source on every claim.
How it works
- 1. Connect
Pull in your sources
Start from your git repositories — Oracus.ai clones them and auto-extracts specs and dependency maps. Layer on Jira and Linear tickets, JUnit results, Confluence pages, OpenAPI specs, GitHub PRs, and uploaded docs. Bring your own connector via the ingest API.
- 2. Embed
Index and link
pgvector + your provider of choice (OpenAI, Anthropic, Google, Ollama). Sources get cross-linked so a question about a ticket surfaces the matching test, spec, and doc.
- 3. Ask
Grounded, cited
Every claim cites a source id. The prompt enforces it; the response schema requires it. Hallucinations are structurally hard.
Know who shipped what
Connect your GitHub org alongside Jira or Linear and Oracus.ai stitches them together. Every ticket gets resolved to the engineers and teams that worked on it — and every answer can name them.
"PLAT-91 was shipped by @ana on the Payments team" beats "the engineering team owns this." Oracus.ai pulls org members, team memberships, and PR authorship and joins them against Jira assignees and reporters.
"What does the Payments team work on, and who runs it?" returns recent ticket history, current in-flight work, and a named tech lead — straight from Slack, no onboarding doc required.
A claim that shows up in both systems — same person assigned the ticket and authored the PR — beats either signal alone. Cited owners come from the intersection, not a single weak source.
› @Oracus.ai who can I ask about our 3DS payment flow?
@miguel built it in SHOP-50 (Payments team). There are 2 open follow-up tickets owned by @ana, and the test suite around it is currently passing.
Read-only GitHub access is enough — Oracus.ai only needs the org's member, team, and PR metadata to build the ownership graph. No code is ingested through this integration; that comes from the git repository connector.
Read the GitHub integration docsWhy now
Engineering orgs have grown beyond the point where one person remembers everything. Tickets, tests, specs, and docs rot independently. Oracus.ai is opinionated about grounding so the answers stay honest, even when the corpus is messy and contradictory.
One Docker image, one Postgres, your API keys. Sensitive context never leaves your network.
Vercel AI SDK underneath. Swap models per task. Bring your own embeddings.
The model never gets to skip evidence. The prompt enforces it; the schema requires it.
A single container you run inside your own network. Your data, keys, and model calls stay on your servers — nothing phones home.
Talk to sales
Pricing scaled to your repo count, a guided demo, or a chat about how Oracus.ai would fit your stack. Leave your email and we'll be in touch.