The intelligence layer beneath your AI.
cgiCore sits between enterprise data and AI systems — assembling governed, contradiction-aware, domain-specialized context so your models, assistants, and agents perform like specialists.
What cgiCore is, and isn’t.
The enterprise AI stack is crowded with chatbots, vector stores, and agent frameworks — each one built to specialise in a single task. In the agentic era, we asked a different question: why stitch together a dozen narrow agents when one agent, fed governed context across every domain, can do the work of an entire enterprise? We call it Context General Intelligence — one agent, a variety of reasoning engines, the full surface of a business.
What connects to cgiCore.
Six surfaces orbit the engine. Your knowledge and a council of specialists feed it. Models route through it. Everything it emits is traced, structured, and governed.
Four pillars of governed context.
Each pillar is treated as a first-class subsystem, not a feature flag. Together they turn raw enterprise data into context your models can trust.
Domain specialization
Load a specialized domain into cgiCore and any downstream AI becomes stronger in that domain — without changing a single model weight.
Contradiction intelligence
Our reasoning engines surface logical conflicts across your knowledge base, then route them through review, policy, resolution, or deferral.
Provenance
Every fact is traceable to its source — which session, which document, which extraction run, which author. Nothing enters context anonymously.
Private deployment
Runs inside your VPC, on-prem, or private cloud. Your data, reasoning artifacts, and audit log never leave customer-controlled infrastructure.
A context layer, not a replacement.
cgiCore acts as the LLM proxy for your stack, routing to whichever model fits the job — a frontier model for reasoning, an efficient model (e.g. a codex-style CLI) for mapping and organizing data to save tokens. Your agents keep their existing interfaces; cgiCore assembles the context, routes the call, and writes anything new back.
- 01 Your agent calls
/v1/chat/completionsas usual. - 02 cgiCore infers the relevant domain and assembles context.
- 03 Multi-signal reasoning is combined into a weighted context packet.
- 04 A tight, provenanced context packet is sent with the prompt.
- 05 Anything new from the interaction is written back under policy.
This wasn’t built overnight.
Over a decade ago, we were building ML systems for financial markets — pattern recognition, signal analysis, execution models. The models worked. What didn’t work was getting them to share what they had learned.
Every model was an island. Context died between sessions. Decisions couldn’t be traced. We didn’t have a word for it then, but what we were fighting was context decay — and in high-stakes environments it was expensive every single day.
So we kept fixing it. Persistence layers. Governance layers. Audit trails. Refined year after year in environments where every decision had to be explainable to a regulator, a counterparty, or an auditor.
Then foundation models arrived, and the industry hit the same wall we’d been pushing against for a decade: models that couldn’t remember, agents that contradicted each other, outputs that couldn’t be explained. cgiCore is that decade of enterprise software engineering — now taking shape as a product, governed by design, purpose-built to sit beneath modern AI stacks.
Context as infrastructure, not an afterthought.
Most enterprise AI failures aren’t model failures — they’re context failures. Here’s what shifts when the context layer is governed instead of glued together.
What we compare
RAG + agents, glued together
Governed context layer
Qualitative comparison. Specific behavior depends on domain, data quality, and deployment — we benchmark against your stack during pilot, not on generic datasets.
See cgiCore running against your domain.
We’ll walk through your data surface, pick a pilot domain, and show you how context quality — not prompt tuning — becomes your enterprise AI advantage.