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Research.

Models keep getting smarter. The context they run on does not. We work on the AI context layer, the discipline of grounding intelligence in institutional knowledge so it stops decaying, drifting, and quietly disappearing. Institutional knowledge loss is the failure mode nobody names until it is too late.

Synq researches the AI context layer: the problem of keeping enterprise AI grounded as systems change, knowledge decays, and agents forget. Open problems include context drift, provenance, sovereign retrieval, durable multi-agent memory, and grounded evaluation.

THE THESIS

Intelligenceislargelysolvedandincreasinglycommoditised.

Contextisnot.Itisspecifictoyourorganisationanditdecays.

Institutionalknowledgelossissilent:peopleleave,systemschange,provenancedisappears.

Groundingmodelsinlivingcontext,withprovenance,isthepathtoreliableandsovereignAI.

OPEN PROBLEMS

The unsolved problems beneath enterprise AI.

Context drift markerA panel on the left connected to a panel on the right by a line that breaks partway across, drawn as a link going stale.A panel on the left connected to a panel on the right by a line that breaks partway across, drawn as a link going stale.

The AI context layer goes stale as systems and facts change; grounding must stay current.

Knowledge decay markerFour stacked bars of decreasing length, drawn as a store of knowledge thinning over time.Four stacked bars of decreasing length, drawn as a store of knowledge thinning over time.

Institutional knowledge loss as people leave and tools change; capturing it before it disappears.

Provenance and citation markerThree nodes on a single path with an arrow running back along it, drawn as an answer traced to its source.Three nodes on a single path with an arrow running back along it, drawn as an answer traced to its source.

Every grounded answer needs a traceable path back to source.

Sovereign retrieval markerA closed boundary with a node and a retrieval path entirely inside it, and no line crossing the boundary.A closed boundary with a node and a retrieval path entirely inside it, and no line crossing the boundary.

Sovereign AI that keeps retrieval and data inside your boundary.

Multi-agent memory markerThree panels around one shared node, each connected to it, drawn as agents reading from a common memory.Three panels around one shared node, each connected to it, drawn as agents reading from a common memory.

Shared, durable memory so agentic AI does not relearn the same context each run.

Grounded evaluation markerA pair of panels on a shared baseline with a check mark against one, drawn as an answer measured rather than assumed.A pair of panels on a shared baseline with a check mark against one, drawn as an answer measured rather than assumed.

Measuring whether an answer is actually grounded, not just fluent.

Knowledge graph construction markerScattered loose marks on the left resolving into a connected lattice of nodes on the right.Scattered loose marks on the left resolving into a connected lattice of nodes on the right.

Building an enterprise knowledge graph from messy real-world sources.

Access and governance markerA grid of cells with some cells outlined and a gate on the path into it, drawn as permissioned access.A grid of cells with some cells outlined and a gate on the path into it, drawn as permissioned access.

Permissioned, audited access to context across an organisation.

HOW IT WORKS

How the AI context layer works.

Written to be equally clear to the buyer signing off on it and the engineer shipping against it.

Capture

Pulls in documents, data, and decisions from across the organisation.

Structure

Cleans and normalises messy sources into consistent form.

Graph

Builds the enterprise knowledge graph of entities, relationships, and decisions.

Retrieve

Fetches the right context for a query with precision.

Ground

Anchors the model's answer in that context, with citations.

Serve

Delivers grounded answers and agentic actions to the people and systems that need them.

Governance and provenance

PAPERS AND BENCHMARKS

What we are publishing.

We have not published a research record yet. These are the working titles and abstracts of what is coming, listed here so the direction is honest and public before the work is finished.

PAPERFORTHCOMING

Measuring grounding: an evaluation for context-faithful answers.

A proposed evaluation method for checking whether a model's answer is actually grounded in retrieved context, not just fluent.

Coming soon
BENCHMARKFORTHCOMING

A benchmark for enterprise knowledge graph construction from messy sources.

A benchmark design for testing how reliably real-world, inconsistent sources can be structured into a usable knowledge graph.

Coming soon
PAPERFORTHCOMING

Context drift: keeping an AI context layer current as systems change.

An approach to detecting and correcting context drift as the underlying systems and facts an AI depends on keep changing.

Coming soon
NOTEIN PROGRESS

Provenance by construction: citations from source to answer.

A working method for attaching verifiable provenance to every claim a grounded answer makes, from source document to citation.

Coming soon
PAPERFORTHCOMING

Sovereign retrieval: keeping context inside the boundary.

A retrieval architecture for sovereign AI that keeps context and data inside an organisation's own boundary.

Coming soon
BENCHMARKFORTHCOMING

Durable multi-agent memory across sessions.

A benchmark for testing whether multi-agent systems retain shared context across sessions instead of relearning it each run.

Coming soon

LAB NOTES

Notes from the work.

Why every enterprise AI pilot fails

The failure is not in the model. It is in the missing context layer every enterprise AI deployment needs to succeed.

Read

India's GCCs are sitting on the most valuable AI training data in the world

Global Capability Centers process the world's enterprise workflows. The data they generate is the rarest dataset in AI, and it is not being captured systematically.

Read

What teams get wrong about grounding

Early notes on why a fluent answer and a grounded one are not the same thing, and why the difference matters in production.

Coming soon

Working notes: building an enterprise knowledge graph

Observations from structuring messy, real-world enterprise sources into something a model can actually use.

Coming soon

What sovereignty actually requires in practice

A working note on what keeping retrieval and data inside an organisation's boundary really involves, beyond the pitch.

Coming soon

WORK WITH US

Work on reliable AI with us.

Grounded models, provenance, and a context layer that keeps up. If that is the problem you work on too, we would like to talk.

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