Persistent Deal Memory
A canonical, versioned deal story graph that survives across meetings, interfaces, model versions, and seller changes.
The architecture of autonomous CRM
Three layers. One deal intelligence engine.
SAIQ is the revenue-specialized reasoning layer between the AI your team talks to and the CRM your business runs on.
Conversational layer
Claude · ChatGPT · Copilot
Provides the general-purpose reasoning and conversational interface.
Revenue-specialized reasoning
SalesAssistIQ
Maintains the persistent deal story, runs qualification against your commercial ontology, and evaluates true stakeholder state — governed and reproducible.
Control plane
Your CRM platform
Executes governed workflows, permissions, and cross-system data federation — ServiceNow, Salesforce, HubSpot, or any CRM you already own.
Verified account & deal briefs
Fit scores vs. your deal-breakers
Next best action, pushed daily
CRM updated for the rep
One remote MCP server — SAIQ's deal intelligence renders natively in Claude, ChatGPT, Slack, Teams, and your CRM's AI surface. One integration, every surface.
CRM platforms
ServiceNowSalesforceHubSpotMonday.com
AI & work surfaces
ClaudeChatGPTCopilotSlackTeams
Third-party names are the property of their owners; SAIQ is an independent layer, not an endorsement.
How it works
How SAIQ builds every deal's living story.
A deal is not a row in a table — it is a living story evolving over time. Three layers of intelligence keep it current, reasoned, and grounded.
Continuous Research
Every signal, every sourceMonitors accounts and ingests signals continuously — meeting transcripts, emails, stakeholder changes, company news, earnings calls. No manual logging, no data entry.
Deal Reasoning
From signal to next best actionMaps stakeholders, scores the opportunity against your commercial ontology, and recalculates only the delta when something changes — pushing the next best action daily, with the reasoning traceable behind every score.
Insider Insights
Grounded in real-world perceptionGrounds each deal in Aniline's perception data — 1B+ employee-insight data points plus public disclosures — so context reflects reality, not guesswork.
The real differentiator
Six things prompting can't replicate.
A general AI agent on top of your data is a demo. These six are the difference between a demo and a system your revenue org can run on.
Commercial Ontology
Product intelligence packs with atomic value drivers, scoring models, buyer personas, disqualifiers, and bundle logic.
Verified Stakeholder State
Not just names — currentness verification, role certainty, influence hypotheses, decision relevance, and freshness status.
Delta Reasoning
After each new signal — meeting transcript, email, news event — recalculate only what changed, expose the implication, and update the deal story.
Governed Autonomy
Human-in-the-loop pause/resume, structured outcome classes, approval routing, traceability, and explainability.
Evaluation & Reproducibility
Versioned workflows, measurable confidence markers, evidence displays, and backtesting against real deal outcomes.
Persistent deal memory
Generic AI
Re-explain the deal from scratch, every session.
Picks up the deal's story exactly where it left off.
If missing
You'd notice instantly — you'd be the one holding the context, not the system.
This is the one a competitor can't catch up on later. Copying the schema takes a month; recreating two years of captured deal history is impossible, because the moment it happened is gone the moment it's not captured. A late arrival can't go back and record the champion's comment from a meeting that already ended.
Commercial ontology
Generic AI
Reasons from the public internet, not your catalog.
Scores every deal against your actual value drivers and disqualifiers.
If missing
It would recommend things your own product can't back up.
Product-aware reasoning is the cleanest version of this claim — it starts from what you actually sell and works outward to the accounts that have the pain, not the other way around.
Verified stakeholder
Generic AI
Lists whatever names it's told, with no way to check them.
Verifies who still holds the role and flags when a champion goes quiet.
If missing
It would still list someone as a live stakeholder six months after they left the company.
A stale stakeholder repeated confidently is worse than no answer at all — it's the specific failure mode that costs a deal, not just a research hour.
Delta reasoning
Generic AI
Re-reasons from scratch every time, at full cost.
Recalculates only what changed since the last signal.
If missing
Every question would cost the same and take as long as the first one, even when nothing changed.
Re-deriving everything per question is token-economically absurd at portfolio scale. A maintained story amortizes; a fresh session every time doesn't.
Governed autonomy
Generic AI
No approval routing, no pause point, no outcome classes.
Pauses for human sign-off outside a set confidence threshold, full audit trail.
If missing
There'd be no way to know whether a recommendation needed a human's sign-off before it acted.
This is what makes enterprise buyers comfortable letting AI act at all — never silent failure, never hallucinated certainty.
Eval & reproducibility
Generic AI
Ask it twice, get two different answers, no way to know which to trust.
Same input, same rigor — versioned workflows backtested against real outcomes.
If missing
You'd get a different answer every time you asked, with no way to know which one to trust.
Two sellers asking the same question should get one governed answer with the same citations, not two different guesses on two different days.
Any LLM + SAIQ beats any LLM alone.
ChatGPT, Claude, Copilot, and Gemini are brilliant generalists that answer any question once. SAIQ is the specialized system that knows every deal's story, enforces your sales methodology, operates continuously, and writes governed intelligence back to your CRM.