The Operating Intelligence Pipeline
An educational breakdown of how Composed AI ingests business data, structures knowledge, enforces evidence, and powers active decision layers.
Data Lifecycle Architecture
How information evolves from unorganized business records into automated operational actions.
Raw tickets, product usage logs, documents, and chats.
Unified, versioned data layer approved by your team.
Grounding validation layer to prevent LLM hallucinations.
Continuous pattern recognition and anomaly detection.
Evidence-backed querying and persistent context logs.
Grounded customer-facing resolutions and human handoffs.
Supervised API webhooks and automated workflow loops.
Technical & Business Specifications
A granular view of every layer inside Composed AI, explaining the technology and its business justification.
Knowledge Base
The unified, structured repository that establishes your organization's source of truth.
Connects to Zendesk, Shopify, Notion, and databases via read-only sync workers. Raw contents are processed, chunked, and vector-embedded, stored in cryptographically isolated database namespaces to enforce strict workspace boundaries.
Ensures that all client knowledge, company policies, and product details live in a single, versioned workspace, eliminating information silos and stale resources.
Evidence Engine
The critical grounding validation middleware that prevents AI hallucinations.
Uses semantic search similarity thresholds combined with deterministic regex mapping to trace every chatbot assertion back to exact, approved source chunks. If confidence scores fall below 78%, responses are flagged.
Gives managers complete confidence that the AI will never fabricate answers or misquote refund policies, ensuring absolute reliability.
Signals
Automated, real-time pattern discovery across customer conversation streams.
Runs clustering algorithms over incoming ticket data streams every hour. Filters single-incidence noise and groups matching customer issues into structured Signals, complete with frequency metrics and impact scores.
Alerts operations leads to emerging issues (e.g. refund requests spiking 40% after a minor release) before support queues are overwhelmed.
Persistent Investigations
Persistent thread logs that archive institutional knowledge and decisions.
Stores complete contextual query chains, selected source snapshots, and remediation status tags inside a relational PostgreSQL state tree.
Builds reusable operational playbooks. When an issue reoccurs six months later, the team has the exact analysis, evidence trail, and solution on record.
Ask Composed
An evidence-backed, natural language query interface for business operations.
Executes semantic queries across the isolated vector index, injecting selected context files into the LLM context space alongside strict instructions to respond with citations only.
Enables product managers and support leads to ask complex questions (e.g., "What are users complaining about in workspace onboarding?") and get instant, cited answers.
Chatbot (Customer AI)
A customer-facing automated support assistant grounded in your approved knowledge.
A lightweight Javascript widget and API interface that serves grounded responses. Intercepts queries, applies the Evidence Engine validation, and escalates to Zendesk or Intercom when questions fall outside the Knowledge Base.
Safely deflects 40-70% of tickets with zero risk of hallucinations, seamlessly routing complex conversations to human agents with full context.
AI Readiness
The automated quality checkpoint ensuring performance standards are met before publication.
Evaluates chatbot configurations against a test set of historical queries. Generates scores from 0 to 100 on accuracy, grounding, and hallucinations, automatically blocking deployment if thresholds are breached.
Acts as a robust safety gate. You know exactly how well the AI chatbot will answer customer questions before it goes live.
An Operational Scenario
How a ticket surge moves through the platform to resolve root causes and update customer-facing bots automatically.
Ingestion & Discovery
A customer support log syncing worker records 45 refund inquiries in 2 hours. The Discovery Layer identifies this cluster, flags it as a Signal ("Refund Spike"), calculates its segment value, and alerts the support manager.
Investigation
The support lead queries Ask Composed: "What did we release in our Shopify catalog this week?" Ask Composed reviews catalog commits alongside ticket transcripts and reveals a pricing label discrepancy on shipping fees, referencing the exact files.
Remediation
The product team updates the shipping policy document and synchronises it back to the Knowledge Base. AI Readiness automatically evaluates the update against historical ticket responses to confirm grounding accuracy scores.
Resolution
Customer AI receives the updated context and begins resolving shipping policy questions instantly. The Signal volume declines back to baseline, and the persistent thread is saved as a verified operational runbook.
Technical Deep Dive FAQs
Technical inquiries on safety, model usage, latencies, and deployments.
How does Composed AI ensure data security and tenant isolation?
We implement strict logical tenant isolation. Each workspace has its own dedicated schema within our database and isolated namespace in our vector storage. Your data is encrypted at rest using AES-256 and in transit using TLS 1.3. We never share data across customer workspaces.
Are public foundation models trained on our uploaded knowledge?
No. Composed AI maintains a strict data privacy guarantee. We use API connectors to access LLM reasoning capacities, and our data agreements explicitly dictate that your data is never used to train, tune, or improve third-party foundation models (including OpenAI, Anthropic, and Google models).
How does the human support handoff work technically?
When Customer AI detects a question it cannot ground in the Knowledge Base (or if a customer requests a human), it calls our integration webhooks. This instantly triggers a ticket creation in Zendesk, Intercom, or Slack, passing the complete conversation transcript and the exact evidence trail to the human agent.
What is the latency on the Evidence Engine verification?
The semantic validation and grounding verification add minimal overhead, averaging between 120ms to 250ms depending on the density of the citation search, ensuring customer chatbot response speeds remain under 1.5 seconds.
How does Composed AI process multiple languages?
Composed AI leverages Gemini models via Vertex AI to natively ingest and process files, tickets, and event logs in 40+ global and Indian regional languages (e.g. Spanish, German, Japanese, Hindi, Tamil, Bengali). The Discovery Layer analyzes documents in any language, extracting signals and standardizing the final alerts in English to maintain operational consistency. The Customer AI chatbot widget automatically detects the visitor’s browser locale or entry language and responds grounded in that language.