Synq Context
The data intelligence layer your enterprise runs its AI agents on.
One deployment. Full enterprise context. Every agent, every engine, every automation understands how your company actually works.
Deploy in 30 Days →How the layer works.
The Engines Layer
BD, Finance, HR, Data, and Physical engines sit on top of the context layer. They handle workflows - generating pipelines, producing reports, screening candidates, scheduling operations. Every query they make is answered with real enterprise context, not generic model knowledge.
The Context Layer
Synq Context captures, stores, and serves enterprise knowledge to all agents in real time. It ingests data from every tool, builds a knowledge graph of your company, captures expert reasoning, and exposes everything through a single query API - so every agent is always working from the same source of truth.
Six components. One unified layer.
01
Ingestion Pipeline
Connects to your existing stack - CRM, HRMS, ERP, comms - and ingests workflow traces continuously. Supports 50+ connectors out of the box. New systems onboarded in under a day.
02
Knowledge Graph
Builds a queryable map of your enterprise - entities, relationships, decision patterns, and tribal knowledge. Automatically updated as new data flows in. Every query returns context grounded in your company's reality.
03
Expert Reasoning Capture
Records not just what happens, but why - the expert judgment and decision logic behind each outcome. Encodes senior decision-maker heuristics before they walk out the door. The rarest form of enterprise knowledge, finally persistent.
04
Context Query Engine
Natural language interface for AI agents to query your enterprise knowledge in real time. Agents ask questions in plain English and receive accurate, company-specific answers. No prompt engineering required.
05
Context API
Standard REST API for any AI system, engine, or automation to access enterprise context on demand. Plug any LLM, agent framework, or automation tool into the same source of truth. Zero duplication, zero drift.
06
Self-Improvement Loop
Every agent query + outcome feeds back to improve context accuracy and model performance. The layer gets smarter every day it runs - more data, better reasoning, tighter feedback loops. Your AI advantage compounds over time.
03 - deep dive
Expert Reasoning Capture
The four-step methodology: observe expert decisions in context, elicit the implicit reasoning behind them, encode it into machine-queryable form, and deploy it as live agent knowledge.
04 - deep dive
Context Query Engine
Every AI engine - BD, Finance, HR, Ops - queries Synq Context in plain English and gets answers grounded in your company's actual data. Who approves this? What's the SOP? How do we price? Answered from real enterprise knowledge, not generic model outputs.
06 - deep dive
Self-Improvement Loop
Every agent action feeds back into the layer - outcomes are logged, data is stored, and the context layer is updated. Each cycle makes every subsequent query smarter. Your AI advantage compounds automatically over time.
30 days from contract to live layer.
Week 1
Audit & Connect
- –Full stack audit across all enterprise systems
- –Ingestion pipeline configured and connected
- –Initial data mapping and schema validation
- –Stakeholder interviews to identify key workflows
Week 2
Build the Layer
- –Knowledge graph construction from ingested data
- –Entity and relationship mapping complete
- –Expert reasoning capture sessions begin
- –First context queries tested internally
Week 3
Deploy Engines
- –First AI engine deployed on the context layer
- –Context API live and accessible
- –Initial agent queries running in production
- –Feedback loop instrumented and active
Week 4
Optimize & Scale
- –Performance calibration and accuracy review
- –Additional engines onboarded
- –Team training and handover
- –30-day success review and scale plan
Works with your existing stack.
50+ connectors. No rip-and-replace. Just plug in and go.