AI for sales and marketing
We build AI for sales and marketing teams and the agencies that work with them: account research and lead qualification for sales, brand-checked content and reporting for marketing, and automation of agency delivery. Agents prepare the work, and a person approves what goes to a prospect, a customer or a client.
Industries in sales and marketing
- Sales teamsAccount research, lead routing, call notes and enablement answers for B2B sales teams.
- Marketing teamsBrand-checked content, cross-channel reporting and first-party data for marketing teams.
- Marketing and creative agenciesDrafts from briefs, client reporting and search across past work for agencies.
The work, and where software fits.
Sales and marketing work runs across a CRM, ad platforms, an email tool, a website and a lot of shared documents. Much of the day goes on moving information between them: researching accounts, logging calls, rebuilding reports and rewriting content for each channel.
Software helps most where that work repeats and the inputs already sit in your systems. We build agents and integrations on HubSpot, Salesforce, Google Ads, GA4 and the other tools your team uses, with a review step for anything sent to prospects or published under your name.
How we improve sales and marketing.
Account research and lead qualification
Briefs written before the first call, and inbound leads enriched, scored against your ideal customer profile and routed to the right rep.
CRM data people can use
Call notes, next steps and deal fields captured from calls and emails, so pipeline reviews and forecasts start from current data.
Content with a review step
Drafts written against brand guidelines and an approved claims library, delivered to your CMS for an editor to approve.
Reporting across channels
Ad platform, analytics and CRM data in one warehouse with consistent campaign names, and commentary a marketer edits before it is shared.
Search across past work
Proposals, case studies, campaigns and enablement material found by meaning, with the source document linked.
Related work.
Further reading.
Human-in-the-loop AI agents: design patterns for production
The patterns we use to run AI agents safely in production: approval checkpoints, confidence thresholds, tool permissions, audit trails and fallbacks.
4 min read
LLM evals: how to test AI features before every release
A practical approach to LLM evals: build a test set from real cases, combine code checks with model grading, and block releases that regress.
5 min read
How to build a production RAG chatbot: a practical guide
What it takes to turn a promising RAG experiment into a chatbot people trust: ingestion, hybrid search, citations, permissions and evals.
5 min read
Common questions.
We set them up to draft. A rep sends outreach and an editor publishes content, and only messages your team wrote as templates, such as a meeting confirmation, go out automatically.
Yes. We build on HubSpot, Salesforce, Pipedrive and Microsoft Dynamics 365, and on Google Ads, Meta Ads, LinkedIn Ads, GA4, Klaviyo and the common CMS platforms through their APIs. If a tool has no usable API, we say so in the first week and plan around it.
It stays in your own systems and EU cloud regions. Agents fetch CRM data at the moment they need it, we use model providers on business terms that exclude training on your data, and we keep personal data out of prompts where the task allows.
Start with the built-in features, such as HubSpot Breeze or Salesforce Agentforce, when the work stays inside that CRM. A custom build makes sense when the process crosses several tools, follows your own qualification or brand rules, or needs data the CRM does not hold. Our build vs buy guide sets out the trade-offs.