All Benchmarks

Your agents are only as good as the signal they work with.

Go-to-market output improves with better signal. That's Amdahl.

800K tokens of noise vs. 2K tokens of signal.

That’s not a prompt engineering problem. It’s an infrastructure problem.

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
Example cluster output: Pricing confusion, trending +34% quarter over quarter. Representative quote: I don’t understand how our usage credits roll over month to month… Source acme/disc-2024-03-12.vtt line L218, 47 quotes.
context used
2,000 tokens

Deterministic · Consistent · Attributed

400× fewer tokens  ·  higher accuracy  ·  every claim cited

Same 200 calls. Different approaches.

Raw approach
  • 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
Pre-processedPreferred
  • 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 13+ tools. When teams build AI agents on top of that:

01Agent reads call transcripts from the Gong API200 calls
02Everything enters the context window800K tokens
03Model attention degrades~30% into window
04Key facts get lost in the middle-30% accuracy
05Agent hallucinates patterns from a recency-biased samplefalse patterns
06Team ships content that misses the signal
Effective window

30–60%

of the advertised window

Long-context models reason reliably over only a fraction of their window. (RULER, NVIDIA 2024)

Lost in the middle

30%+

accuracy drop

Answers degrade sharply when relevant facts sit in the middle of long context. (Liu et al., 2023)

Retrieval recall

~50%

past ~32K tokens

Needle-in-haystack recall falls off sharply as prompts grow. (Anthropic + Gemini 1.5 evals)

Four problems, four fixes.

01Problem

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.

800K tokens in30%+ accuracy lossU-shaped attention
01Fix

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.

2K tokens out16ms query timeCited to source
02Problem

RAG retrieves chunks, not answers

Trend and signal questions need counting, correlating, and clustering across thousands of conversations. Chunk retrieval can't do analysis.

Chunks, not analysisNo cross-referencingNo measurement
02Fix

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.

10 classifiers/message970+ auto-discovered clusters100K+ messages enriched
03Problem

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.

Proven inevitableAll architectures failNo engineering fix
03Fix

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.

Confidence-scoredDeal-correlatedSource-linked
04Problem

Enterprise data lives in 13+ tools

CRM, calls, tickets, and threads live in different tools with no shared schema. Each holds a partial view, so agents give partial answers.

90% unstructured37 tools avg50%+ siloed
04Fix

One schema, every source, 67-second freshness

Connectors ingest every source into one speaker-attributed, deal-joined schema, 67 seconds from source system to queryable.

13+ OAuth connectors67s source-to-indexSpeaker-attributed
The researchStanford logoStanford·Lost in the MiddleMicrosoft logoMicrosoft·GraphRAG 3x over RAGKarpathy logoKarpathy·Context EngineeringMIT logoMIT·The Price of MeaningMIT CSAIL logoMIT CSAIL·Beyond Context LimitsLangChain logoLangChain·Context Engineering for AgentsChroma Research logoChroma Research·Context Rot

See Amdahl on your own data.

Try it for free
claude plugin marketplace add amdahlco/amdahl-cookbook; claude plugin install amdahl-gtm@amdahl-cookbook