Amdahl provides a unified data platform that aggregates structured CRM and analytics data with unstructured conversational data such as call recordings and support logs, then applies machine‑learning classifiers, clustering, and pattern mining to generate real‑time, cited battle cards, positioning briefs, and loss playbooks. The service delivers these evidence‑based artifacts via a web console or API, integrates with any LLM through its MCP REST API, and offers custom apps for tailored sales workflows.
Funding
Funding not disclosed
Founders
Product
Problem
GTM agents often rely on generic context connectors that ingest large volumes of raw transcripts and CRM data, leading to inconsistent, over‑generalized answers, high token usage, and untraceable claims. This hampers sales teams’ ability to deliver accurate, data‑backed insights during calls and reduces win rates.
Solution
Amdahl offers a Customer Intelligence Context Engine that unifies structured sources (CRM, analytics) with unstructured content (call recordings, support documents) into a searchable, citation‑ready layer. The platform applies machine‑learning classifiers, clustering, and pattern mining to surface deterministic answers that reference specific source artifacts, dramatically lowering token consumption. Agents access the enriched context via an MCP REST API, allowing them to replace existing context engines without altering their underlying AI models. The engine delivers consistent, high‑quality outputs such as real‑time battle cards, auto‑optimized talk tracks, and call preparation materials, all with traceable citations. Amdahl’s tiered pricing lets teams start with a free tier and scale to enterprise‑grade deployments with custom apps and self‑hosted options.
Target Audience
Primary customers are B2B GTM teams—sales, revenue operations, and enablement groups—that deploy AI‑powered agents for call analysis, battle‑card generation, and sales content creation.
Features
- Unified ingestion of CRM, analytics, call transcripts, support docs, and other natural‑language sources
- ML‑driven classification, clustering, and pattern mining to create a structured context layer
- Deterministic, cited answers that link each claim to the originating call, record, or document
- MCP REST API for seamless integration, enabling swap of the context engine under existing agents
- Token‑efficient processing delivering up to 400× fewer tokens per query
- Built‑in security: SOC 2 Type II compliance, OAuth 2.0 read‑only integrations, tenant isolation, and optional VPC‑isolated deployment
- App library with pre‑built and custom GTM applications (battle cards, positioning, enablement, etc.)