Trust your GTM agents to deliver winning outputs.
One API that knows what good looks like for your business.
Tell me some use cases for amdahl.ai, based on what you know about my work. You can use https://amdahl.ai and https://docs.amdahl.ai for reference.
Your agents can now check every draft before it sends, find who wins and why, test page copy against your buyers, see where revenue is lost, and stay grounded at every client.
Outbound agent (Agent guardrail): Writes, gets checked, holds what fails. You read the few that miss, not all 50. Example run: search.query("what do buyers like Acme care about?"); returns 9 cited quotes · closed-won only; evals.run(draft_47); verdict 2 of 5 checks, "saves you time" contradicted by 9 quotes; returns prompt fix kept; verdict 4 of 5 checks · pass
Research agent (Deal research): ICP, pains and competitors, answered from your own deals and calls, with the quotes. Example run: search.query("why do enterprise deals stall in security review?"); returns 6 cited quotes · 4 accounts · ranked by relevance; search.query("what changed for buyers like Acme this quarter?"); returns 9 cited quotes · 5 min fresh
Website agent (Positioning check): Checks page copy against what your buyers say, with the quotes. Example run: evals.run(homepage_hero); verdict 2 of 5 checks, generic positioning; returns suggestions returned · 3 quotes cited
Pipeline agent (Win and loss): Win drivers, loss reasons and stalled deals, with deal numbers and quotes. Example run: search.query("why did Q3 enterprise deals stall?"); returns 14 deals · security review is the stall · 6 quotes
Client agent (After handoff): Deployed at your client, checked against their own deals. Example run: search.query("what changed in their pipeline this week?"); returns 14 new signals · 5 min fresh; evals.run(weekly_digest); verdict 4 of 5 checks · pass, current with their deals
corpus · 12,480 graded signals
Amdahl helps your agent deterministically understand what resonates with your buyers.
Amdahl models your sales history, then evaluates and optimizes both the output and the prompt behind it.
How it works: your agent, your LLM and your prompt, asks Amdahl what matters to this buyer and gets the evidence back. It writes the draft; Amdahl grades it and sends fixes back until it is approved, from one of five checks passed to five of five. Only approved drafts ship, and the prompt fix is kept, so the next run starts smarter.
01 · Deterministic
Pattern recognition
Amdahl understands every call, email and CRM record you have and models what works with your buyers, surfacing patterns nobody thought to look for.
02 · Evidence
Evals
Is it AI slop, or will it land with this buyer? Every draft is checked against what works for your GTM. So you don't have to manually review and investigate every draft.
03 · Learning loop
Optimization
Drafts that miss get rewritten, and so does the prompt behind them. Your own data feeds back into the prompt, so every draft starts from what's working, not from scratch.
Your data stays yours.
Your data
Amdahl
Every record is tagged across ten dimensions: sentiment, persona, quality, deal stage, competitive signals, topics, psychographics, segment, outcome, confidence.
pricing objection
38% of stalls
security review
week 6, always
champion left
2.1x loss rate
- Buyer insights
Buyer insights
Ask what your buyers care about. Get answers cited to the exact call, email or deal.
product→
- Evals
Evals
Evaluate outbound or page copy against what works for your business. Get each check, the quotes behind it, and an optimized prompt.
product→
- MCP
MCP
Plug Amdahl into any agent, so it answers from your buyer data with citations.
connect→
Your stack
- Your context layer
- Your agents
- Your team
See it on a real draft.
Your agent's draft
AI draft · cold email
To: Dana, VP Eng · Northwind
Hi Dana - saw Northwind is scaling fast. We help engineering teams ship 10x faster with best-in-class rollout tooling. Worth a quick 15 minutes next week?
Amdahl grade
Fail1 of 5checks passed
- Relevant positioningFail
- GroundingFail
- Verified specificsFail
- DifferentiationFail
- CTA clarityPass
What Amdahl found
- Contradicts "10x faster"
“Speed honestly isn't our problem. ... The problem is that four people have to sign off.”
Gongcall i.4821l94VP Engclosed lost
- What won instead
“What moved people was showing the audit trail.”
Gongcall i.5108l41championclosed won
- Lead with this
“Every release waits on four sign-offs, and nobody can say who approved what.”
HubSpotopp ent-204discovery noteclosed won
Prompt fix · kept for the next run
Lead with the four sign-offs, not speed. Name the audit trail as the proof. Drop “10x faster” and “best-in-class.”
Go-to-market, like an engineer.
Quality is usually treated as subjective. Amdahl makes it a deterministic objective, built from how your buyers move through your funnel.
"A lot better than any message that I could build with Claude directly, while staying hands off."
8/10
questions won
Same model, two data layers: a blind judge scored Amdahl higher on 8 of 10 GTM questions.
2.6x
more consistent
Same question, five runs: 73% of the numbers came back the same, against 28% without Amdahl.
5 mins
data freshness
5 minutes end-to-end data processing latency.
400x
fewer tokens, worked example
200 calls is about 800K tokens of transcript. The cluster an agent needs is about 2K.
Get started.
Getting started: optionally connect sources such as Gong, HubSpot, and Salesforce; install the API or the MCP server at https://app.amdahl.ai/mcp; then let your agent run. Each draft comes back graded, with the fix, so your agents improve every run.
01
OptionalConnect your data
One-click connect to Gong, HubSpot, Salesforce.
30+ integrations
02
Install the API or MCP
Your team uses the MCP. Your agent calls the API.
claude mcp add --transport http amdahl https://app.amdahl.ai/mcp03
Let your agent run with it
Each draft comes back graded, with the fix.
Agents that improve every run
Know what wins before your agent ships.
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