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RevXAI
Inside Intelligence · Calibration

Your team's facts steer the platform.

AI alone hallucinates. Calibrated AI inherits your team's specifics — your ICP, your competitors, your discovery questions, your sequence cadence — and uses them across every profile, brief, sequence, and reply suggestion.

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The problem

Why generic AI gets your company research wrong.

An LLM with no constraints produces what looks like an ICP. It's a plausible English paragraph that could describe almost anyone. Paste it into your team WhatsApp group and a Saudi GM will smirk at it within 30 seconds. The problem is not the model — it's that the model doesn't know what you actually sell, in your team's words; which customers you've actually closed; which competitors you've actually lost to in the last six months; or which discovery questions actually open a Saudi enterprise account.

  • What you actually sell, in your team's framing.
  • Which customers you've actually closed — and which look-alikes really matter.
  • Which competitors you've actually lost to in the last six months.
  • Which discovery questions open a Saudi enterprise vs. an Egyptian SMB.
  • Which sequence cadence works for your channel mix.
How it works

Three moves. Then every artifact sharpens.

Calibration is not a feedback button. It's structured fact injection at the top of the model's context window, with explicit authority precedence.

  1. 01

    Confirm what the AI got right

    After your first profile generates, every fact the AI extracted is presented as a calibratable item. Confirm the ones it nailed. Edit the ones it almost got. Reject the ones it invented.

  2. 02

    Add what the AI couldn't know

    Drop in your own facts. Our actual ACV is twelve thousand, not fifty. We lost three deals last quarter on pricing, not features. Our cold-email opener that converts is X. The model sees these on the next generation across all four pillars.

  3. 03

    Regenerate — or run a fresh artifact

    Confirmed and edited facts move to the top of the source-authority hierarchy. The model is required to defer to them. Run the same profile again, generate a new brief, draft a fresh sequence — every downstream artifact inherits your calibration.

Authority hierarchy

Where your facts sit in the model's context.

When the model writes your profile, brief, sequence step, or reply suggestion, it walks this stack in order. The hierarchy is enforced at prompt construction — not as a post-hoc filter.

  1. 01

    Calibrated facts

    Your team's confirmed facts. Highest authority. The model is required to defer to them.

  2. 02

    Workspace history

    Prior profiles, briefs, sequences, and edits in this workspace.

  3. 03

    Scraped primary sources

    The company's own website, fetched at request time.

  4. 04

    Web search results

    Third-party context retrieved at request time — three scoped searches per profile.

  5. 05

    Provider data

    PDL, Coresignal, Apollo, Lusha — normalized into the context pack.

  6. 06

    Model priors

    The model's own training data. Lowest authority. Used only when nothing above answers.

Before vs. after

What calibration changes — across the platform.

Each row is the same target company, run twice. The only difference is whether the calibration loop ran.

  • Profile · ICP
    AI alone

    Mid-market SaaS companies.

    Calibrated

    Saudi B2B SaaS, 50–200 employees, hits a scale crisis at 1,000 daily orders. Decision committee includes a Tech-led champion plus CFO approval.

  • Brief · Differentiation angle
    AI alone

    Product offers similar features at a competitive price.

    Calibrated

    Lost three of five to {{Competitor}} in Q1 — they win on local-bank settlement; we win on multi-currency reporting and PDPL-aligned PII handling.

  • Sequence · Email opener
    AI alone

    Hi {{Name}}, I came across {{Company}} and was impressed by…

    Calibrated

    السلام عليكم {{الاسم}}، لاحظت تعيين CRO جديد لديكم في مارس — في تجربتنا، فرق فينتك في هذا الحجم تُعيد تقييم بنية البيع خلال أول ٩٠ يوماً…

What you can teach RevXAI

Eight categories of facts the model will defer to.

Calibration is incremental. Start with one ICP. Add a competitor next week. Every artifact across all four pillars sharpens with every fact you teach it.

  • ICP definitions

    Named segments with role, company shape, and buying trigger.

  • Customer roster

    Sanitized — names, segment, and ACV band only.

  • Competitors

    Who you actually compete against, and where each one beats you.

  • Discovery library

    Questions that open accounts, in Arabic, English, or both.

  • Sales plays

    Pain hypothesis, opening line, qualifying questions.

  • Buyer roles

    Titles, motivations, objections — across the buying committee.

  • Differentiators

    The things you actually win on, in your team's words.

  • Sequence patterns

    Channel mix, cadence, and openers that converted in your data.

Calibration FAQ

Three questions we hear every week.

  • Is calibration the same as RAG or fine-tuning?

    No. RAG retrieves documents at query time. Fine-tuning adjusts model weights. Calibration is structured fact injection at the top of the context window, with explicit authority precedence. Faster than fine-tuning, more reliable than RAG retrieval, far cheaper to maintain.

  • What if I calibrate something that turns out to be wrong?

    The model defers to your calibrated fact even if the public web disagrees. This is intentional — your team's internal facts are often more current than any public source. If you change your mind, edit or remove the calibration item and regenerate.

  • Is calibration per-profile, or workspace-wide?

    Workspace-level. Every profile, brief, sequence draft, and reply suggestion across all four pillars inherits the calibration. Edits to one artifact do not back-propagate; if you discover a new fact while reviewing an artifact, promote it to a calibration item with one click.
Calibration runs across the platform

Every pillar inherits your facts.

Calibrate once. Sharpen everything.

Generate a profile, confirm what's right, add what the AI couldn't know. Free workspace, no card.

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