Industry

SaaS AI, built on
your telemetry.

Sales research, qualification, developer support and churn signals — built on your product telemetry and your API specification, with a person on every send.

Where it breaks today

Outbound that scales linearly

Pipeline grows in step with the number of people researching accounts and writing first emails, and the research is the part that consumes the day.

Churn scored, not acted on

A ranked list of at-risk accounts arrives with no instruction attached, is read once and ignored — and six months later someone concludes churn prediction does not work here.

Support answering from stale docs

Lead qualification, onboarding and developer support are labour-intensive and inconsistent, and a support answer drawn from a docs site can describe a parameter that was deprecated two releases ago.

OUR APPROACH

The model prepares; a person sends

A software company already holds the two things that make AI useful in go-to-market and support: product telemetry that shows what customers actually do, and an API specification that says what the product actually does. We build on those. A churn score becomes a specific observation with a specific suggested action in the CRM. A developer question is answered from the spec, the SDK source and the changelog rather than from a docs page that may be out of date.

On the outbound side, agents research target accounts from public sources and from enrichment providers whose terms permit it, and draft personalised outreach for a representative to approve. Sending stays with a person, opt-outs are honoured, and the unsolicited-email rules that apply in each market — ePrivacy and Spain's LSSI in the EU, among others — are respected by design rather than by exception.

HOW IT DEPLOYS

PHASE 01

Wire the GTM stack

CRM, product telemetry, calendar and support systems are connected into a single layer with your ideal customer profile and playbooks encoded.

PHASE 02

Activate the agents

Account research, lead qualification and spec-grounded developer support go live — drafting outreach for approval, routing prospects, and answering with a citation.

PHASE 03

Turn scores into actions

Usage telemetry is monitored continuously; an at-risk account produces a specific, drafted action for customer success to take, and a holdout proves whether it retains revenue.

What we build

Sales Research Agents

Agents that research target accounts from public filings, news and enrichment sources whose terms permit it, draft personalised outreach for a rep to approve, and book meetings — within the consent and B2B-marketing rules that apply in each market.

SEE SALES AGENTS →

Lead Qualification

Inbound leads scored against firmographics and intent signals, with high-intent enterprise prospects routed to account executives and smaller accounts entering nurture sequences configured and approved by your team.

SEE LEAD QUALIFICATION →

Developer Support

Support agents grounded in your API specification, SDK source and changelog, helping diagnose customer code snippets with generated examples validated against the spec before they are returned.

SEE DEVELOPER EXPERIENCE →

Churn and Expansion Signals

A classical model scores account risk from depth of use; a language model assembles what changed and when, and lands a specific, drafted action in the CRM for customer success to take.

SEE CHURN AND EXPANSION →

Controls we implement

GDPR-ALIGNED CONTROLS CCPA-ALIGNED CONTROLS TENANT DATA ISOLATION SUB-PROCESSOR LIST READY OPT-OUT HONOURED NO TRAINING ON CUSTOMER DATA

These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security certifications, and the audits and attestations your sector requires remain yours to run — see our trust centre.

Saas use cases

SAS.01

Shipping AI Inside Your Product

Building an AI feature into software other people depend on: latency budgets, cost per seat and graceful failure.
Open page
SAS.02

Churn & Expansion

Predicting account risk and expansion from product telemetry, and acting on it before renewal.
Open page
SAS.03

Developer Experience

Docs, SDK support and integration help that answer from your actual API surface rather than from a stale index.
Open page

Questions

Questions about SaaS & Technology.

What does an AI sales research agent actually do?

It researches a target account from public filings, news and enrichment providers whose terms permit the use, assembles a short brief on why this account and why now, drafts outreach that a representative reads and approves, and books the meeting once a reply comes in. It does not scrape sources whose terms forbid it, it does not send unsupervised, and it honours opt-outs. Unsolicited email to individuals in the EU is governed by ePrivacy and, in Spain, the LSSI, with a limited B2B exception — the sequence is configured to respect those rules in each market, and your own legal review of the consent basis still applies.

How does AI-powered churn prevention work for SaaS?

A classical model scores account risk from product telemetry — depth of use rather than login counts — and a language model assembles the narrative: what changed, when, and what to do about it. That lands as a specific action in the CRM rather than a score in a dashboard. Whether it retains revenue is proven with a holdout, which is the only honest way to know.

Can AI agents provide technical support for developer products?

Yes, and the important detail is what they are grounded in. Answers come from your API specification, SDK source and changelog — artefacts that are correct by construction when generated from the running system — rather than from a docs site that may describe a deprecated parameter. Generated examples are validated against the spec before they are returned, and are a starting point for the developer rather than a guarantee.

How are inbound leads qualified automatically?

Every inbound lead is scored against firmographics, intent signals and behavioural data from your own product. High-intent enterprise prospects are routed to account executives immediately; smaller accounts enter nurture sequences that your team has configured and approved, so no lead sits unworked and nothing is sent that a person has not signed off.