What AI tools do you use?

We build on top of the tools you already have - HubSpot, Salesforce, n8n, Make - and layer in AI from OpenAI, Anthropic, and others where it makes sense. No proprietary platform, no new vendor to manage.

Is this actual AI or just rule-based automation?

Both, depending on the job. Simple routing and alerts are rule-based - that's the right tool for that. Scoring, summarization, intent detection, and research use real LLMs. We match the approach to the problem.

Do I need to replace my CRM?

No. We build on top of what you have. Whether that's HubSpot, Salesforce, or something else, we connect to it - we don't replace it.

Do I need a technical team to manage this?

No. The automations run on their own. We build it, hand it off, and your team uses the outputs - alerts, scores, summaries - without touching the infrastructure.

How do you handle our data?

Your data stays in your stack. We don't store it, sell it, or route it through proprietary systems. The pipelines we build connect your existing tools directly.

What does onboarding actually look like?

A 30-minute discovery call, then we scope the work and send a proposal. Once you're in, we go heads-down - no lengthy kickoffs or status meetings. You see working software fast.

What if the AI gets it wrong?

It will sometimes, especially early. That's why we build human review steps where it matters and tune based on your data. Accuracy improves over time - it's not set-and-forget.

How is this different from HubSpot's or Salesforce's native AI?

Native AI works within the walls of that one tool. We connect your entire stack and build logic that runs across it - enrichment, scoring, alerts, and summaries that pull from multiple sources and trigger actions anywhere.

How to Fix CRM Data Quality With AI

Your CRM data is bad. Not “could be better” bad. Structurally, operationally bad in ways that are silently undermining every decision you make about your pipeline.

The average B2B CRM has 30-40% of contact records missing critical fields like job title or company size. It has deals marked Active that haven’t had activity in weeks. It has close dates that slipped months ago but were never updated. It has duplicate contacts, orphaned companies, and custom properties that were populated once during an import and never touched again.

Every report you pull from that CRM is wrong. Every forecast is wrong. Every lead score built on that data is wrong. And the solution isn’t better rep discipline - it’s agents that fix the data continuously without requiring a human to do anything differently.

How bad is CRM data quality, really?

Pull up your HubSpot right now and run three checks.

Missing fields: Filter contacts by “Company Size is unknown” or “Job Title is empty.” In a typical CRM with 5,000+ contacts, you’ll find 1,500-2,000 with missing critical fields. These aren’t old dead leads. Some of them are contacts on active deals. Your reps are selling to people whose basic information you don’t have recorded.

Stale deals: Filter deals by “Last Activity Date is more than 21 days ago” AND “Deal Stage is not Closed Won/Lost.” In a pipeline of 60 deals, you’ll typically find 12-15 that haven’t had any activity in three weeks but are still showing as active pipeline. Your forecast includes these. It shouldn’t.

Slipped close dates: Filter deals by “Close Date is in the past” AND “Deal Stage is not Closed.” These are deals where the expected close date passed and nobody updated it. In some CRMs, this is 20-30% of open pipeline. Your forecast model is using these dates. The dates are fiction.

Every one of these problems makes your pipeline reporting less accurate, your lead scoring less reliable, and your forecasts less trustworthy.

Why can’t you fix CRM data with rep training?

You’ve tried. Every RevOps team has tried. You sent the email about filling in MEDDIC fields. You built the dashboard showing CRM completeness scores. You brought it up in the team meeting. Maybe compliance improved for two weeks. Then it went back to baseline.

This isn’t a discipline failure. It’s a system design failure.

Reps are paid to sell. Every minute they spend updating CRM fields is a minute they’re not spending on a call, a follow-up, or a demo. The incentive structure actively discourages the behavior you need. Threatening consequences for bad CRM hygiene doesn’t change the math - it just adds resentment.

The reps who do update their CRM consistently are the exception. Building your data strategy around the exception is how you end up with a CRM that’s 60% complete on a good day.

The fix isn’t changing human behavior. It’s removing humans from the data capture loop entirely.

How do AI agents fix CRM data?

Three types of agents handle the three categories of bad data:

Enrichment agents fix missing fields. When a new contact enters HubSpot - from a form fill, a meeting booking, a manual creation - an agent immediately enriches it. Job title, company size, industry, tech stack, funding stage, LinkedIn URL. It pulls from enrichment APIs, cross-references sources, and writes structured data back to the CRM. The contact record is complete before the rep even opens it.

For existing contacts with gaps, the same agent runs a backfill sweep - batching through your database, enriching records that are missing critical fields, flagging the ones where no data could be found. A one-time cleanup that takes hours instead of weeks.

Hygiene agents fix stale and inaccurate data. A deal that hasn’t had activity in 14 days gets flagged - not on a dashboard, but as a Slack alert to the AE with the specific gap. A close date that’s passed without an update triggers a task for the rep to either update it or close the deal. A contact whose job title changed on LinkedIn gets their CRM record updated automatically.

These agents run continuously. They don’t wait for the quarterly CRM audit. They catch problems the day they happen.

Deduplication agents fix structural data problems. Duplicate contacts, orphaned company records, contacts associated with the wrong company. The agent identifies likely duplicates based on name, email, and company matching, then either merges automatically (for clear matches) or flags for human review (for ambiguous cases).

What changes when your CRM data is actually clean?

Everything downstream gets better.

Lead scoring works. When firmographic data is complete and accurate, scoring models can actually distinguish fit from noise. Your MQL-to-SQL conversion rate improves because you’re routing on real data instead of gaps.

Forecasting works. When stale deals are flagged and close dates are current, your pipeline report reflects reality. Your CRO stops discounting the forecast by 30% because they’ve learned not to trust it.

AI agents work. Every AI agent you build - deal risk, pre-call briefs, competitive intelligence - is only as good as the data it reads. Clean CRM data isn’t just nice to have. It’s the prerequisite for everything else.

Rep trust improves. When reps open a contact record and the data is already there - enriched, current, complete - they start trusting the CRM instead of keeping their own spreadsheets. The system becomes useful instead of bureaucratic.

Clean data unlocks everything else. Compare AI vs manual CRM enrichment approaches, see how clean data powers AI lead scoring that actually predicts who will buy, and learn how MEDDIC agents populate qualification fields automatically.

How to run a CRM data audit in two days

Before building agents, you need to know exactly what you’re fixing. A structured audit takes two days and gives you a clear prioritized list of the most impactful data problems to tackle first.

Day 1: Pull the numbers. Run five HubSpot filters and record the results.

Contacts with no job title: how many? Contacts with no company size: how many? Active deals with no activity in 21+ days: how many? Open deals with a past close date: how many? Duplicate contacts (same email domain, same name variant): estimated count from a dedupe tool scan.

Write these numbers down. This is your baseline. Every number here is a problem an agent can fix.

Day 2: Prioritize by impact. Not all missing data is equally damaging. Rank your problems by which ones affect live deals most.

Missing job title on active deal contacts matters more than missing job title on unqualified leads. Stale close dates on high-value deals matter more than stale close dates on low-probability pipeline. Duplicates on active accounts matter more than duplicates on dead leads.

Build your agent rollout in the order your audit prioritizes. Fix the most expensive problems first.

How to build the enrichment agent

The enrichment agent is almost always the right first build. It fixes the most common problem (missing fields), proves value quickly (data completeness is measurable), and improves every other agent in your stack.

The architecture: an n8n workflow triggered by HubSpot’s contact creation webhook. When a new contact is created, the workflow calls your enrichment API of choice - Clearbit, Apollo, or a custom combination. It receives structured data back, maps each field to the appropriate HubSpot property, and writes it back via HubSpot’s API.

For existing contacts with gaps, the same workflow runs as a batch job - querying HubSpot for contacts where company size is empty, paginating through the results, enriching each one, writing back. A 5,000-contact backfill typically runs in 2-4 hours.

The enrichment source matters. Clearbit has the highest coverage for US tech companies. Apollo is broader but lower accuracy. For most B2B SaaS companies in the US, start with Clearbit for firmographic data and supplement with Apollo for contacts Clearbit can’t find. If cost is a constraint, Apollo’s enrichment API is more affordable.

Build the new-contact trigger first. Get it running and verified. Then run the backfill.

What data quality level you need before building other agents

You don’t need perfect data to start. You need good enough data for each specific agent to function.

Deal risk detection needs: last activity date (logged reliably), deal stage (being updated), close date (set at deal creation and updated when it slips). If these three fields are reliable, the risk agent works.

Lead scoring needs: company size, industry, and lead source for at least 60% of your contact records. Below 60%, the model’s accuracy degrades enough that it produces noise instead of signal.

Pre-call brief agents need: deal records with contact associations intact (no orphaned deals), and at least one logged activity per deal so the agent has something to summarize.

Run the enrichment agent for 30 days before building scoring or risk detection. That 30-day window dramatically improves the data quality every subsequent agent runs on.

Your CRM isn’t a data problem. It’s an infrastructure problem. Agents that enrich, monitor, and clean your data continuously are the infrastructure most teams are missing.