For engineers

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

Every GTM team is building AI agents for outbound, content, research, and deal analysis. The hard problem isn’t the model — it’s giving agents the right data, from the right sources, at the right time.

Amdahl pre-processes your GTM data into structured, cited intelligence before any agent touches it. 800K tokens of noise becomes 2K tokens of signal.

The problem

The fundamental issue isn’t the model. It’s the context.

Enterprise GTM data lives across 13+ tools. When teams build AI agents on top of that:

text

Agent reads 200 call transcripts from Gong API
  → 800K tokens
  → model attention degrades after ~30% of window
  → "lost in the middle" drops accuracy 30%+
  → agent hallucinates patterns from recency-biased sample
  → team ships content based on a mirage

enterprise data

90%unstructured

siloed across tools, no unified schema

avg tools

37per employee

each enterprise employee uses daily

data shared

50%+never shared

of organizational data never crosses teams

Four problems. Four fixes.

01Context rotProblem

Context windows don't scale

Even 1M-token models reason effectively over only 30–60% of their window. Accuracy drops 30%+ when key info sits in the middle. Your 200 call transcripts become 800K tokens of noise where the model sees the beginning, the end, and hallucinates the rest.

800K tokens in30%+ accuracy lossU-shaped attention

01Fix

Pre-processed intelligence fits in 2K tokens

The same 200 calls run through a pipeline: PCA dimensionality reduction, UMAP embedding, recursive sub-clustering with JSD divergence scoring. Output: 120 measured clusters. A query returns the relevant cluster with exact quotes, deal correlations, and trend data — ~2K tokens with room for reasoning.

2K tokens out16ms query timeCited to source

02RAG gapProblem

RAG retrieves chunks, not answers

"What objections are trending?" returns 5 random chunks mentioning "objection." "Which accounts show buying signals?" returns CRM note fragments with no cross-referencing. These aren't retrieval problems — they're analysis problems that require counting, correlating, and clustering across thousands of conversations.

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: sentiment, topics, personas, intent, entities, competitors, psychographics, quality, relationships, and vector embeddings. Chi-squared feature scoring identifies statistically significant patterns. Your agent queries pre-computed intelligence, not raw text.

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

03Semantic crowdingProblem

RAG and knowledge graph failure is mathematically inevitable

Formal proofs show any system retrieving by semantic similarity will suffer forgetting and false recall as the knowledge base grows. This isn't a tuning problem. It's a geometric property of how meaning is represented. Knowledge graphs fail too — tested across five architectures, none escape.

Proven inevitableAll architectures failNo engineering fix

03Fix

Structured facts that don't degrade at scale

The research identifies one viable path: pair semantic retrieval with exact structured records. Amdahl pre-computes cluster hierarchies with confidence scores (0–1), deal-outcome correlations, and verbatim quotes linked to source transcripts. The structured output is the episodic record — your agent searches it semantically, but the underlying facts are exact.

Confidence-scoredDeal-correlatedSource-linked

04Data silosProblem

Enterprise data lives in 13+ tools

CRM in HubSpot. Calls in Gong. Tickets in Zendesk. Threads in Slack. 90% unstructured, no unified schema, 50%+ never shared across teams. Each tool holds a partial view. Your agent gets a partial answer.

90% unstructured37 tools avg50%+ siloed

04Fix

One schema, every source, 67-second freshness

OAuth 2.0 connectors ingest from 13+ sources into a unified interactions table. Speaker attribution maps messages to CRM contacts. Deal metadata (stage, amount, outcome) joins at ingest time. 67-second sync lag from source system to queryable index via MCP or REST API.

13+ OAuth connectors67s source-to-indexSpeaker-attributed

The research

Stanford—Lost in the Middle

Morph—Context Rot

Microsoft—GraphRAG 3x over RAG

Karpathy—Context Engineering

Sentra—The Price of Meaning

LangChain—Context Engineering for Agents

The math

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

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

Today

Your Agent

reads everything↓

200 call transcripts

800,000 tokens

Accuracy: unreliable

With Amdahl

Your Agent

queries intelligence↓

Amdahl Pipeline

returns relevant cluster↓

1 cited cluster + quotes

2,000 tokens

Accuracy: cited & verified

400x 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

What comes out the other side

Structured intelligence with citations.

Not documents. Not chunks. Every answer is measured, every claim is cited, every pattern is traced to specific conversations and deals.

Example cluster output

json

{
  "cluster": "Security compliance concerns in enterprise deals",
  "trend": "+34% quarter-over-quarter",
  "confidence": 0.89,
  "deal_correlation": "present in 73% of closed-won enterprise deals",
  "representative_quotes": [
    {
      "text": "We can't move forward without SOC 2 documentation",
      "speaker": "VP Engineering, Acme Corp",
      "deal": "$180K ARR, Stage 4",
      "sentiment": "blocker"
    }
  ],
  "related_clusters": ["procurement_process", "data_residency"]
}

We can't move forward without SOC 2 documentation^[1]

VP Engineering$180K ARR, Stage 4Acme Corp

This is what an agent can reason about. Not 200 raw transcripts — a structured, measured, cited answer to a specific question.

The pipeline

Not a wrapper on an LLM. A real data pipeline.

10 pipeline stages. Runs in BigQuery, tenant-isolated at the dataset level. Deterministic outputs — same input produces same enrichment. SOC 2 Type II certified.

Replicate

OAuth 2.0, 13+ sources, 67s lag

Unify

Single schema, speaker attribution, CRM join

Enrich

10 ML classifiers per message

Discover

PCA → UMAP → recursive clustering

The interface — MCP + API

MCP + REST API. Your agent connects in minutes.

MCP for any compatible client (Claude, Cursor, Windsurf). REST API for custom agents and integrations. Same intelligence layer, two access patterns.

data explore

Schema + sample values

data query

SQL over interactions

data search

Semantic search

data cluster_search

Pattern discovery

context ask

Cited answers

content generate

Voice-matched content

Connection vs. quality

An agent connected to raw Gong transcripts gets noise. An agent connected to Amdahl — via MCP or API — gets pre-analyzed intelligence with citations.

Why this matters now

Three things are converging.

01Adoption

Agents are going mainstream

Every GTM team is building or buying AI agents for content, outbound, research, and analysis. The question isn’t whether — it’s how well they work.

02Scaling

Context windows aren’t scaling fast enough

Even at 1M tokens, effective reasoning caps at 30–60% of the window. Enterprise data sets are orders of magnitude larger. The gap is widening.

03Differentiation

Better data beats better prompts

When every team has the same foundation models, the differentiator is the quality of information those models can access. Context engineering is the job.

The companies that build a real intelligence layer — not just RAG, not just a vector database, but a structured, pre-analyzed, continuously-updated understanding of their customers — will have agents that actually work. Everyone else will have agents that hallucinate confidently.

Connects to your tools in minutes. Pipeline starts immediately. Your agents get structured intelligence via MCP or API.

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