Your agents are only as good as the signal they work with.
Raw transcripts drown an agent. Amdahl models what works with your buyers, then evaluates and optimizes what the agent writes.
800K tokens of noise vs. 2K tokens of signal.
The fix is better evidence, not a bigger window.
Today
200 call transcripts
Inconsistent · Overgeneralized · Unverifiable
With Amdahl
Grounded · Consistent · Cited
Same 200 calls. Different approaches.
- 200 calls × 4,000 tokens = 800K tokens
- Exceeds most context windows
- Lost-in-the-middle degrades accuracy 30%+
- No structure, no citations
- Plausible but unreliable output
- Same calls → pipeline → 120 clusters
- Relevant cluster: ~2K tokens
- Exact quotes, deal outcomes, trends
- Fits in context with room for reasoning
- Cited, measured, verifiable output
The problem isn’t the model. It’s the context.
Enterprise GTM data lives across many tools. When teams build AI agents on top of that:
30–60%
of the advertised window
Long-context models reason reliably over only a fraction of their window. (RULER, NVIDIA 2024)
30%+
accuracy drop
Answers degrade sharply when relevant facts sit in the middle of long context. (Liu et al., 2023)
~50%
past ~32K tokens
Needle-in-haystack recall falls off sharply as prompts grow. (Anthropic + Gemini 1.5 evals)
Four problems, four fixes.
Context windows don't scale
Models reason well over only part of their window. 200 calls become 800K tokens of noise, and the middle gets hallucinated.
Pre-processed intelligence fits in 2K tokens
The same calls arrive pre-clustered. A query returns the one relevant cluster, with exact quotes and deal correlations, in about 2K tokens.
RAG retrieves chunks, not answers
Trend and signal questions need counting, correlating, and clustering across thousands of conversations. Chunk retrieval can't do analysis.
10 ML classifiers per message, pre-computed
Every message is tagged across 10 dimensions before any query runs, so your agent queries computed intelligence instead of raw text.
Semantic retrieval degrades as data grows
Formal proofs show forgetting and false recall are inevitable at scale, across every architecture tested. It's geometry, not tuning.
Structured facts that don't degrade at scale
The one viable path: exact structured records your agent searches semantically. Confidence-scored, deal-correlated, quoted to source.
Enterprise data lives in many tools
CRM, calls, tickets, and threads live in different tools with no shared schema. Each holds a partial view, so agents give partial answers.
One schema, every source, about five minutes fresh
Connectors ingest every source into one speaker-attributed, deal-joined schema, about five minutes from source system to queryable.
