Software Category

AI Sanitisation Software for Consulting Firms

AI sanitisation software automatically detects and removes client-confidential content from consulting deliverables, so a firm can safely reuse its past decks and feed them to AI. AI sanitization software (both spellings are in common use) works at the level of meaning rather than keywords: it reads each element of a slide and decides, per element, whether to redact, replace, obfuscate or keep it.

Also known as deck redaction, document blinding, slide scrubbing, client anonymisation or de-identification, the category has one job. Make confidential decks safe to reuse without destroying the analysis inside them.

Knovari is AI sanitisation software built by strategy consultants, for the consulting use case. It removes only the client-tied elements, down to colour-coded shapes and Harvey balls, and preserves the reusable insight a generic tool would either delete wholesale or miss entirely.

Why it matters now. Nearly every consulting deliverable contains confidential content, and many contain financials or M&A data. Cross-client contamination, content from one client inside another client's deliverable, turns up more often than firms expect. Feed decks like these to an AI assistant as-is and you risk handing one client's confidential position to another through your own tools.

Who it's for

Management consulting firms producing PowerPoint-heavy client deliverables. The people who own or feel the problem:

Heads of Knowledge Management and CKOs curating a knowledge base people can actually reuse.
Heads of AI and Innovation trying to ground AI on the firm's real IP, not the thin slice that happens to be public.
CIO / CDO and their teams (Head of Data/AI, AI programme leads) who need governance and auditability: proving to an auditor why an AI agent behaved as it did, and unblocking AI from low-value "safe" content onto high-value confidential content.

If your firm is racing to deploy AI and the blocker is that your best knowledge is trapped in client-confidential decks, this is the category you are shopping for.

Capabilities

Sensitive data discovery. Knovari detects confidential and client-identifying content across PowerPoint decks and, at project scale, across a whole corpus of files: text, charts and quantitative data, logos and visual identifiers, and embedded objects and metadata. Mode A (flag-for-review) is the discovery workflow: the platform scans a deck, flags every element it judges confidential, and proposes a treatment for a consultant to review before anything changes.

Audit and compliance reporting. Every detection and redaction decision is logged, building a full audit trail a compliance or knowledge-management team can review after the fact. Human-review workflows sit alongside the automated detection, so a person signs off before content moves. Knovari's SOC 2 Type II audit is in process, with controls and evidence live now in the Vanta Trust Centre at trust.knovari.ai.

Two ways to run it: flag-for-review and auto-redact

Sanitisation software fails in practice when it is bolted on as an optional extra. Opt-in workflows stall at partial consultant adoption. Knovari runs in two modes so a firm can start with control and move to coverage.

Mode A: flag-for-review (human in the loop). The software scans a deck, flags every element it judges confidential, and proposes a treatment (redact, replace, obfuscate, keep). A consultant reviews and approves. Every change is logged, highlighted and explained. Best for early rollout, sensitive accounts, and building trust with compliance.

Mode B: auto-redact (opt-out at scale). The software sanitises automatically and publishes to the knowledge base, with a window for the author to object before anything goes live. This is the org-wide process change that gets adoption close to 100%, because nobody has to remember to do it. Sold as a workflow, not an add-in.

Most firms start in Mode A on a first batch, tune a treatment profile to their standards, then flip to Mode B once accuracy earns the trust.

Can AI redact charts, logos and images in slides, or only text?

Most redaction tools handle text only. They find words through OCR or pattern matching, and everything visual is left for a human: the standard vendor answer to "what about images?" is manual review. For consulting decks, that is the wrong way round. Charts, logos, colour-coded shapes and harvey balls carry client identity at least as loudly as the text does.

Knovari runs a vision layer alongside text analysis. It detects and sanitises client-identifying visual content: embedded logos, charts built on client data, colour-coded shapes, watermarks and org-chart structures. The sanitisation is surgical. It removes only the client-tied elements and deliberately preserves the framework or insight the visual expresses, where a generic tool would delete the whole page or touch nothing.

How it compares

Stripping a client's name and logo is now table stakes. Generic AI, keyword tools and manual review all manage it. The hard part is reading the whole slide rather than just its text, reaching code-level traces and preserving reusable IP. Here is how the options compare on the criteria that decide whether a sanitised deck is actually safe to reuse.

Criterion	Generic AI (ChatGPT / Copilot / Claude)	PII / keyword redaction tools	DIY build (Presidio / internal LLM)	Manual review	Knovari
Analyses the slide, not just the text (charts, logos, colour-coded shapes and layout all carry confidential signal)	No. Text-only analysis. Reading a PPTX reliably takes spatial and visual understanding of the slide: charts, logos, colour-coded shapes, layout. Even a structured setup built around the model cannot rewrite chart XML or embedded workbooks, and leaves no audit trail a compliance team can sign off.	No. Matches names and text patterns only.	Possible, but this is the hard 80%, not the easy prototype.	Yes, a human sees the whole slide. But hours per deck and error-prone.	Yes. Combines vision and text analysis to judge every element on the slide the way a consultant would, including whether a figure is a client's private number rather than a public statistic.
Visual redaction (charts, logos, shapes)	No. Visuals are left for manual review.	Not supported.	Not supported without a major vision build.	Text-and-eyes only; slow and inconsistent at scale.	Built in (vision layer + text analysis).
Reaches code-level traces (embedded chart data, logo URLs, workbook names, metadata)	No. Works on visible text.	No. Never reaches the data behind a chart or the XML underneath.	Rarely built. Round-tripping PPTX at the code level is a large part of a 12 months+ build.	No. A human cannot see an embedded spreadsheet or a logo's source URL.	Yes. Cleans the file at code level, including embedded spreadsheets, workbook names and logo links.
Preserves reusable analysis (removes only client-tied elements)	No. Deletes whole pages or leaves them intact. No surgical middle ground.	No. Blunt find-and-replace destroys context.	Depends entirely on tuning against over-redaction.	Variable. Reviewers over-redact to be safe, destroying IP.	Yes. Surgical. Semantic replacement preserves the storyline and message.
Document-level reasoning (whole-deck context, not box-by-box)	Limited by context and consistency.	No. Processes each text box independently, no memory of slide 3 at slide 30.	Hard to achieve reliably.	Yes in principle, but not repeatably at project scale.	Yes. Models how detail accumulates and where combinations create risk.
Audit trail (every change logged and explained)	No.	Limited.	Only if built.	Manual and inconsistent.	Yes. Every change logged, highlighted and explained for compliance.
Throughput at project scale	Not built for batch document processing.	Fast but wrong.	Depends on the build.	No. Hours per deliverable; headcount and cost climb.	Yes. Automated, minutes per deck rather than hours.

The 20/80 line. Find-and-replace and generic tools handle the surface layer, roughly 20% of what is actually sensitive in a consulting slide. The other 80% is invisible to pattern-matching: non-public figures with no name attached, and the code-level traces underneath. And manual review is not the safe fallback it looks like: manually sanitised decks are routinely still re-identifiable by AI.

You should keep your AI tools. Knovari is not a replacement for ChatGPT or Copilot. It is the prerequisite that makes deploying them safely across your full knowledge base possible.

Security and certification

A vendor security review is where confidentiality software either earns trust or stalls. What Knovari holds:

SOC 2 Type II: in process, estimated completion July 2026. Every control and its evidence is viewable now in the Vanta Trust Centre at trust.knovari.ai. "No report yet" becomes "see everything today."
Independent penetration test: completed. The report is shareable under NDA as part of the full security pack.
Third-party risk assessment: passed. Knovari has passed the vendor risk assessment of a major global consulting firm.
Deploy in your own Azure tenant. The default enterprise model deploys Knovari into the client's own Azure tenant, with the frontier-model instance inside that tenancy, so client data never leaves your infrastructure. Default region is West Europe; other regions are provisionable to support GDPR and EU data residency.

More on the standards behind this: SOC 2 Compliance for Knowledge Management Systems.

Pricing signal

Knovari is priced per slide, with volume pricing at project scale. Engagements start with a scoped, paid proof of concept on a single batch of your own decks, not a free pilot.

Book a scoping call

Size the opportunity first

Before a call, put numbers on it. The knowledge value calculator estimates how much reusable IP your firm is sitting on and what it is worth once it is AI-ready: far more usable knowledge for most firms, every proposal and project doc back in play.

See it on your own decks

The fastest way to judge AI sanitisation software is to watch it work on a real consulting slide. Book a demo and we will run it on an escalating set of examples, from an obvious client identifier to the code-level traces you cannot see.