Grow the business without growing the risk.

IntelCS reviews the case files your team cannot get to: more reviews in less time, every file held to the same standard, and the evidence trail attached to each conclusion. Your exposure falls as your volumes rise — and whatever anyone asks, you can answer it.

Sampling is a risk you cannot price.

Manual review does not scale, so most functions review a sample and hope it represents the rest. The files nobody opened are the ones that surface later — in a regulator's inspection, a tribunal, or a claim.

Meanwhile the visible cost — senior reviewers reading scans, chasing missing documents, reconstructing an evidence trail months after the decision — never appears as a line item, and it rises every time the business grows.

What changes for your function.

Three ways in. One engine.

Walk into any review with the evidence already assembled.

When a regulator, tribunal, auditor or board asks how a conclusion was reached, the answer is already written down: the finding, the rule it was tested against, and the page and line it rests on. No reconstruction, no institutional memory, no scramble.

That holds whether your team uses IntelCS as a first-pass reviewer and handles the exceptions, or as a second pair of eyes over work they have already done.

Runs on infrastructure we own.

The objection that usually stops an AI project in a regulated organisation does not arise here. Ingest, redaction, review, graph construction, retrieval and reporting all run on infrastructure IntelCS owns and operates, on self-hosted models in a single-tenant environment. No case data reaches a commercial AI provider, because none is in the path.

Scanned, OCR'd, mixed-format, PII-bearing. OCR repair and redaction happen here, before anything is reviewed.

Your rule-set — regime, thresholds, required evidence, severity — held as editable data, not compiled logic.

Multi-stage automated review: locate, test against rule, corroborate, challenge, and record what could not be found.

A structured audit report: findings, severity, citations to page and line, and an explicit list of evidence gaps.

The report is the deliverable.

Markdown in, Markdown out — reviewable in your own systems, diffable between runs, and readable by someone who has never seen the tool.

Gaps are stated as gaps. A reviewer can follow any finding back to the source page in one step.

Give us a file your team has already reviewed.

The fastest way to judge Compliance Manager is to run it against a case whose answer you already know, then check the citations line by line against your own conclusions.

Once built, exploration is LLM-free.

Traversal, filtering, and path-finding run against the graph itself — fast, inexpensive, and deterministic. The same question returns the same answer tomorrow, and the evidence is attached either way.

One case, one graph.

Graphs are isolated per case — no cross-case retrieval, no shared entity store leaking one client into another. Entity resolution is PII-aware: identifiers are matched without being exposed downstream.

Point it at a case your team knows inside out.

Then judge it on two things: whether the relationships it found are the ones you would have drawn, and whether the gaps it flagged are the gaps you already worried about.

Redaction and OCR repair are part of ingest

Not a downstream filter. Poor scans are reconstructed and personal data is masked before any content reaches retrieval, so what gets indexed is already fit to be quoted in a report.

Verifiable, not persuasive

Answers are assembled from located passages. Where the pile does not support an answer, DocIntel says so instead of composing one that reads well.

No third party in the path

Same model as the rest of the platform: self-hosted inference on IntelCS-operated infrastructure, in a single-tenant environment, with no case content sent to third-party AI providers.

Ask it something you already know the answer to.

Bring the questions your reviewers ask a bundle every week — including the ones where the honest answer is "the document isn't here" — and check what comes back against the pages themselves.

Bundles arrive as they are: scans, photographs of documents, exported threads, spreadsheets, forms. OCR is repaired, structure is recovered, pages and lines are indexed as addressable positions, and PII is redacted before review.

Either the case is tested against a rule-set your function owns — regime, required evidence, thresholds, severities — or a reviewer asks it questions directly. Rule-sets are editable data; adding a regime does not mean shipping code.

Findings, graph edges, and answers are constructed from located evidence. Anything that cannot be located is reported as an evidence gap or flagged unverified — never smoothed over.

Every output retains its citations, the rule-set version that produced it, and the run record. When someone asks your team to prove it, the trail is already written.

A single-tenant environment on infrastructure IntelCS owns and administers, with self-hosted models. Nothing is subcontracted to a commercial AI API, so the list of parties that can see your case data is one name long.

Case material is stored and processed in the jurisdiction you nominate at contract, and stays there. Because models run inside our own estate, residency is a deployment fact rather than a provider's regional endpoint policy.

Redaction runs at ingest, before review. Entity resolution is PII-aware: identifiers can be matched for graph-building without being surfaced in outputs or logs.

Each case is its own boundary — its own index, its own graph. No cross-case retrieval, so one client's material cannot inform another's review.

Runs are recorded with their inputs, rule-set version, and outputs. A review can be reproduced and compared against a later run of the same bundle.

Bundles are transferred over encrypted channels, retained only for the agreed review and retention window, then deleted on a schedule you set — with deletion evidenced. Nothing is used to train models.

Named-individual access on the IntelCS side, scoped to the engagement, logged per action, and revoked on completion. Your own users authenticate through SSO with role-based permissions your administrators set.

Document sources and case systems connect over standard interfaces; outputs are Markdown and structured data, so reports land in the systems your reviewers already use rather than in another portal.

For security reviewers

A technical one-pager covering our hosting topology, data flows, transfer and retention model, model hosting, and logging is available for procurement and security review — along with the sub-processor position, which is that there are none for inference.

Eight rule-sets. One codebase.

Any review that comes down to a document bundle plus a set of rules is in scope. A ninth vertical is a rule-set and a validation cycle — not a new product.

In regulated industries, risk grows faster than the business does.

IntelCS — Intelligent Compliance Solutions — started from an unglamorous observation about how regulated organisations grow. Every new client, adviser, case or contract adds files somebody is accountable for, and the cost of staying on top of them climbs faster than the revenue that created them.

So functions review a sample, carry the rest as residual risk, and reconstruct the evidence months later when it is questioned. General-purpose AI made that worse, not better — fluent text with no traceable basis, and sensitive files handed to a commercial provider to get it. We took the opposite constraints as our starting point.

We work with a small number of organisations at a time.

Each engagement starts with your rule-set and your own case material, so onboarding is deliberate rather than self-service. If that suits how your function works, start a conversation.

Competitors that hard-code a regime must rebuild for the next one. Our review logic is authored data, so a new vertical is a rule-set and a validation cycle — not a new product line, a new codebase, or a new team.

Running self-hosted models on our own estate removes the objection that stalls most AI procurement in regulated buyers — no commercial AI provider in the chain, one named processor, no sub-processor for inference. It is also a capital and operating position competitors reselling an API cannot claim.

Citation-level evidence is what lets a customer put the tool inside a documented, defensible process — the reason it gets adopted at all. Once a firm's review methodology references our evidence trail, replacing us is a methodology change, not a procurement one.

Compliance Manager, newGraph, and DocIntel are entry points into the same infrastructure. Each lands on a different buyer motion while compounding the same engineering investment.

Investor materials, including the platform overview and infrastructure model, are available on request — separate from the sales process.