You've built GTM agents before,and felt what's missing.

Two weeks in, agents drift. Context resets at every run. No data claim is traceable.

Amdahl is the customer world model for every GTM agent. Every conversation you've already had, structured into the context layer your agents query.

0%

more consistent

Five identical runs, same Claude Opus, same question — standard connectors land at 28%.

+0%

higher quality

Same Claude Opus, same 10 GTM questions — raw connectors vs. Amdahl’s context engine.

0%

cited

Every answer traces back to a specific call, CRM record, or doc — no fabricated statistics.

See how it works

Every GTM conversation. One context engine.

Calls, CRM, meeting notes, support, docs, product usage — pulled together, cleaned, semantically tagged.

Amdahl powers any GTM task.

Amdahl call prep — deal briefing with champion and competitor signals

Sales teams are prepared to win every call in every deal.

  • Real-Time Battle Cards
  • Auto-Optimize Talk Tracks
  • Call Prep
See how sales uses it →

Don’t see your workflow?

Tell us what your team needs.

We ship custom Amdahl apps for design partners in days.

Book 30-min walkthrough

Already building agents?

MCPREST API

Keep your agent workflow.

Swap the context engine underneath.

Talk to us →

Same model. Same questions. Different data layer.

8 / 10

Questions won by Amdahl in a blind benchmark

+0%

Score improvement over standard connectors

4.1 vs 2.4

Amdahl avg vs. standard (out of 5)

Today

Your Agent
skims · guesses · hallucinates↓

200 call transcripts

context used
800,000 tokens

Inconsistent · Overgeneralized · Unverifiable

With Amdahl

Your Agent
queries intelligence↓
Amdahl Pipeline
returns cited cluster↓
Pricing confusion47 quotes

“I don’t understand how our usage credits roll over month to month…”

→acme/disc-2024-03-12.vttL218
context used
2,000 tokens

Deterministic · Consistent · Attributed

400× fewer tokens  ·  higher accuracy  ·  every claim cited
See how Claude, ChatGPT, or Perplexity + Amdahl works

Your agents are only as good as the data you feed them.

We unify your structured data (CRM, analytics) with your natural language data (calls, support, docs) — then run ML classifiers, cluster discovery, and pattern mining across all of it. Your agents get cited answers with attributed quotes, not 200 raw transcripts.

01
SourceSystems of record
02
PipelineEnrich · Extract · Surface
03
ExecutionAgents act on context
03ExecutionAgents act on context02PipelineEnrich · Extract · Surface01SourceSystems of recordFirmographic DataNatural Language DataAMDAHL SITS IN THE LANGUAGE LAYER —COMPLEMENTARY TO FIRMOGRAPHIC TOOLS, NOT COMPETING

What teams ship with Amdahl.

4 weeks → 1 day

Content production cycle at a Series C data company using Amdahl for voice-of-customer content

0.0x

Win-rate correlation when reps surface more first-call objections — from Amdahl running on its own sales data

0%

Churned-customer resurrection in two weeks via context-driven email campaigns

Amdahl is our most trusted strategic collaborator.
Growth leadAI-native B2B scaleup
The content person needs the validation and the sources the most. Amdahl is the only tool I've used that gives that consistently.
Content leadSeries C data company
I don't need Claude to do the ideas. Amdahl does — from our actual customer conversations.
Head of GTMB2B SaaS
SOC 2 Type II
Zero data retention with Anthropic
Tenant-isolated. Dedicated cloud option.
No training on your data.

Backed by

a16z SpeedrunPrecursor Ventures

Your buyers already said it. Amdahl finds it and ships it.

Book a 30-minute architecture review — not a sales demo. We’ll map your stack and show what your context layer would look like.

Don’t take our word for it — ask an AI

ChatGPTClaudePerplexity