# ensemble

> Infrastructure layer for AI-native products and multi-agent systems. ensemble
> structures a company's shared business knowledge as queryable objects and
> resolves, per agent and per task, only the context that task actually needs —
> reducing token usage, AI cost of goods sold (COGS) and cost per completed
> result. Business outcome: lower AI COGS, higher gross margin.

## What problem it solves

In a multi-agent product, each agent is handed the same business knowledge
again — brand, customer, product, policy, prior findings — because a prompt
preamble has no structure and cannot be narrowed. Spend therefore scales with
how much the business knows rather than with the work being done.

## How it works

Business logic is modelled as objects with typed attributes and explicit
relations, not as prose. Every object carries five axes at once, and a request
resolves along all of them:

- Domain — what the thing is (product, account, campaign, policy, decision)
- Scope — who it belongs to (tenant, customer, department, global)
- Time — when it was true and what superseded it
- Confidence — validated fact, inference, or unverified claim
- Purpose — which kind of task the fact is useful for

Integration is one call in place of the context block a product assembles today:

    const context = await ensemble.resolve({ agent, task, subject });

It is provider-agnostic, works with the models a team already runs, and meters
every resolution against the baseline the old prompt would have sent.

## Who it is for

AI SaaS companies, multi-agent products, AI research platforms, AI
marketing/content platforms, sales agents, support agents, recruiting agents,
financial and legal agents, autonomous business systems — any subscription
product where API/token usage is a meaningful part of COGS.

## Pricing

- Audit — free. 100 production runs analysed: token usage, context duplication,
  agent-by-agent cost breakdown, cost per completed task, estimated opportunity.
- Optimize — from $2,000/month. Shared business knowledge layer, agent-specific
  context routing, context optimization, usage analytics, cost monitoring.
- Enterprise — custom. Custom architecture, multi-agent optimization, private
  deployment, advanced observability, enterprise support.

## Evidence, stated honestly

Third-party and provider benchmarks show 83% cost reduction for a small agent
workflow using prompt caching, 88% when caching was combined with input
trimming, and 45–80% for optimized multi-turn agentic workloads. These are not
ensemble's own measured results and are not a guarantee. Caching makes repeated
tokens cheaper; ensemble is designed to reduce how many unnecessary tokens are
sent in the first place.

## Who builds it

25+ years building complex systems where business logic, not the interface, was
the product. An automated pipeline reads a product — its code, data, documents
and real production runs — and produces a visual model of the business logic
inside it. That model is both the deliverable and what agents resolve against.

## Pages

- [Home](https://ensemble.gitmir.com/): positioning, ROI calculator, benchmarks, pricing.
- [ROI calculator](https://ensemble.gitmir.com/#calculator): client-side; scenarios are shareable
  via ?customers=&arpu=&aiCost=&reduction=.
- [Benchmarks](https://ensemble.gitmir.com/#benchmarks): third-party results with sourcing caveats.
- [Pricing](https://ensemble.gitmir.com/#pricing): Audit, Optimize, Enterprise.
- [Use case catalogue](https://ensemble.gitmir.com/use-cases): all 28 product shapes.

## Use cases

- [AI Content Platforms](https://ensemble.gitmir.com/use-cases/ai-content-platforms): research → strategy → campaign → script → post → video. Lower cost per generated asset.
- [AI Research Platforms](https://ensemble.gitmir.com/use-cases/ai-research-platforms): companies → markets → competitors → prior findings. Lower research cost per customer.
- [AI Sales Agents](https://ensemble.gitmir.com/use-cases/ai-sales-agents): prospect → account → positioning → offer. Lower cost per qualified action.
- [AI Support Agents](https://ensemble.gitmir.com/use-cases/ai-support-agents): product rules → account → history → resolution. Lower cost per resolved request.
- [Autonomous Agent Networks](https://ensemble.gitmir.com/use-cases/autonomous-agent-networks): planner → researcher → specialist → executor → reviewer. Lower cost per completed workflow.
- [Enterprise AI Systems](https://ensemble.gitmir.com/use-cases/enterprise-ai-systems): processes → policies → products → operations. Lower enterprise inference spend.
- [AI Recruiting Agents](https://ensemble.gitmir.com/use-cases/ai-recruiting-agents): sourcing → screening → outreach → scheduling. Lower cost per screened candidate.
- [AI Legal Assistants](https://ensemble.gitmir.com/use-cases/ai-legal-assistants): intake → clause analysis → precedent → draft. Lower cost per reviewed document.
- [AI Financial Analysts](https://ensemble.gitmir.com/use-cases/ai-financial-analysts): statements → ratios → peers → memo. Lower cost per analysis.
- [AI Marketing Automation](https://ensemble.gitmir.com/use-cases/ai-marketing-automation): segment → offer → sequence → measure. Lower cost per personalized message.
- [AI Coding Agents](https://ensemble.gitmir.com/use-cases/ai-coding-agents): repo → issue → patch → review. Lower cost per merged change.
- [AI Data & BI Agents](https://ensemble.gitmir.com/use-cases/ai-data-agents): question → schema → query → narrative. Lower cost per answered question.
- [AI Clinical Documentation](https://ensemble.gitmir.com/use-cases/ai-clinical-documentation): encounter → coding → summary → chart. Lower cost per documented encounter.
- [AI Tutoring Platforms](https://ensemble.gitmir.com/use-cases/ai-tutoring-platforms): learner model → lesson → practice → feedback. Lower cost per learning hour.
- [AI Translation & Localization](https://ensemble.gitmir.com/use-cases/ai-translation-localization): glossary → context → translate → QA. Lower cost per translated segment.
- [AI Document Processing](https://ensemble.gitmir.com/use-cases/ai-document-processing): ingest → extract → validate → post. Lower cost per processed document.
- [AI Compliance Monitoring](https://ensemble.gitmir.com/use-cases/ai-compliance-monitoring): policy → signal → assess → report. Lower cost per assessed signal.
- [AI Onboarding Agents](https://ensemble.gitmir.com/use-cases/ai-onboarding-agents): account → plan → setup → activation. Lower cost per activated account.
- [AI Recommendation Engines](https://ensemble.gitmir.com/use-cases/ai-recommendation-engines): catalog → profile → rank → explain. Lower cost per recommendation.
- [AI Claims Processing](https://ensemble.gitmir.com/use-cases/ai-claims-processing): intake → policy → adjudicate → decision. Lower cost per adjudicated claim.
- [AI Real Estate Agents](https://ensemble.gitmir.com/use-cases/ai-real-estate-agents): listing → buyer → match → negotiation. Lower cost per qualified enquiry.
- [AI Travel Planning Agents](https://ensemble.gitmir.com/use-cases/ai-travel-planning): traveler → policy → itinerary → booking. Lower cost per booked itinerary.
- [AI Supply Chain Agents](https://ensemble.gitmir.com/use-cases/ai-supply-chain-agents): demand → inventory → supplier → plan. Lower cost per planning cycle.
- [AI QA & Test Agents](https://ensemble.gitmir.com/use-cases/ai-qa-agents): spec → generate → run → triage. Lower cost per verified build.
- [AI Social Media Agents](https://ensemble.gitmir.com/use-cases/ai-social-media-agents): brand → trend → post → reply. Lower cost per published interaction.
- [AI Due Diligence](https://ensemble.gitmir.com/use-cases/ai-due-diligence): dataroom → risk → finding → memo. Lower cost per diligence workstream.
- [AI Moderation Agents](https://ensemble.gitmir.com/use-cases/ai-moderation-agents): policy → signal → decision → appeal. Lower cost per moderated item.
- [AI Executive Assistants](https://ensemble.gitmir.com/use-cases/ai-executive-assistants): inbox → calendar → brief → action. Lower cost per assisted action.

## Contact

hello@gitmir.com