Your agents can now self-improve on GTM outcomes, compound each other's wins, ship call prep without a data team, understand what drives revenue, and maintain themselves in prod.
Outbound agent (Self-improving drafts): Writes, gets graded, improves - nobody reviews 50 drafts by hand. Example run: evals.grade(draft_47, against: closed_won); verdict 2/5, "saves you time" - 9 lost deals, 0 won; search.query("how do winning reps phrase this?"); returns 9 cited quotes · closed-won only; verdict pass 5/5, revised · nobody edited a prompt
Research agent (Deal research): Cited answers from your deals and calls. 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
Pre-call agent (No data team): Built on enriched, cited deal data. Not raw transcript dumps. Example run: search.query("open objections + competitor mentions, Acme"); returns 8 cited quotes · security review is the stall; evals.grade(call_brief); verdict pass 5/5, every claim cited
Agent fleet (Shared learnings): What one agent learns, every agent retrieves. Example run: evals.grade(outbound draft); verdict pass 4/5, outcome joins the shared corpus; search.query("what won deals like Acme?"); returns your deal-desk agent retrieves it · cited
Client agent (After handoff): Deployed at your client, still learning their deals. Example run: search.query("what changed in their pipeline this week?"); returns 14 new graded signals · 5 min fresh; evals.grade(weekly_outputs); verdict pass 4/5, current with their closed deals
corpus · 12,480 graded signals
Amdahl sits unopinionated under your stack. 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
- Search API
Search API
Ask questions across your buyer data. Get cited answers, retrieved over an ML-enriched corpus.
product→
- Evals API
Evals API
Grade any draft against your buyer data.
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
Go-to-market, like an engineer.
Driven by statistical learning and causal analysis of what your buyers say and how deals move through your funnel.
80% time saved
Chore's marketing team reclaimed their week after moving content production onto Amdahl
3.7x win rate
Reps surface more first-call objections and close more — measured on our own data
100x viral reach
Multiple Chore posts hit 100,000 impressions against a 1,000 baseline
400x
token efficiency
Raw MCPs firehose vast amounts of data into every agent call. Instead, Amdahl serves the most relevant context needed.
1.7x
more accurate
Our ML models achieve accuracy and depth that LLMs miss. No human revisions needed.
2.6x
more consistent
100% of LLM outputs cited in your buyer data. No hallucinated insights or statistics.
5 mins
data freshness
5 minutes end-to-end data processing latency.
Know what wins before your agent ships.
Backed by world-class investors










