Build your own AI sales stack, or buy one that works today?

Your engineers can wire an LLM to your CRM in a sprint. The hard part is everything after: pipelines, prompts, evals, edge cases, and maintenance that never ends. Airspeed ships that work as a product.

• Live in days, not quarters
• From $5K per year, no engineering headcount
• A RevOps analyst on the build

A 30-minute demo and expert Q&A.

Airspeed on G2

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The honest comparison

Both paths get you AI on your calls. They differ in when it works, what it costs, and who carries it.

What the build actually involves

The demo takes a weekend. Production takes a team. Here is the work between the two.

1. Wire up the data

   Call recordings, emails, CRM objects, and tickets, all normalized into one pipeline before a single prompt runs.

2. Build the integrations

   Salesforce or HubSpot writes, calendar and email access, permissions, and rate limits. This is where most internal builds stall.

3. Tune the prompts

   Every sales motion is different. Someone has to iterate on prompts against real calls, then do it again when the model changes.

4. Evaluate the outputs

   Without an eval harness you find out about bad CRM writes from angry reps. Building one is a project in itself.

5. Handle the edge cases

   Multi-language calls, reassigned deals, merged accounts, reps who go off script. The long tail never ends.

6. Maintain it forever

   Model deprecations, API changes, new use cases from sales leadership. The build is never done, so the team never leaves.

What you get on day one

CRM writing, deal summaries, and coaching scorecards, live in your first month.

One brain, every conversation

Every call, email, and ticket lands in a single model of your buyer. No pipelines to design, no data warehouse project first.

CRM that updates itself

Airspeed writes to HubSpot, Salesforce, and other major CRMs after every call. Fields, notes, next steps. No rep login, no custom integration to own.

Agents that act

Stalled deal spotted, re-engagement drafted. Closed-won pattern found, outbound launched.

Why teams buy instead

The second year of owning an internal build costs more than the first.

Weeks to live, not quarters

A forward-deployed RevOps analyst tunes Airspeed to how you sell. Foleon hit payback in under two months.

A subscription, not a headcount plan

Airspeed starts at $5K per year. One AI engineer costs more than that per week, before infra and eval tooling.

Improves with every release

Airspeed improves across 100s of customers. Your internal build only improves when your engineers get back to it.

Your engineers stay on your product

The build pulls senior engineers into GTM tooling for a year. Buying keeps them on the thing your customers pay for.

Skip the build. Keep the outcome.

See Airspeed log the call, update your CRM, and draft the follow-up, in your own pipeline.

When does building in-house make sense?

When GTM tooling is your product, or you have a dedicated ML platform team with spare capacity and an eval culture. For most revenue teams, the maintenance cost is what kills it.

We already started building. Is it too late to switch?

No. Most teams we work with tried an internal prototype first. Airspeed runs alongside it during the trial, so you can compare outputs on your own calls before deciding.

What does Airspeed cost?

Plans start at $5K per year. Pricing scales with team size, and every plan includes the forward-deployed RevOps analyst.

What does it cost to build an AI sales assistant in-house?

Plan for senior engineers, infrastructure, and eval tooling over 6 to 18 months before it ships value, then a team to maintain it as models and APIs change. The build is a sprint. The maintenance is forever.

How long does Airspeed take to deploy?

Teams are live in days. HubSpot, Salesforce, and other major CRM writes work on day one, and you can see Airspeed run on your own pipeline in a 30-minute demo.