How It Works
How CophyAI processes, measures,
and improves enterprise AI workflows.
For technical buyers and engineering teams who want to understand the architecture before they evaluate the product.
Integration
Fits into your stack. No rip-and-replace.
Two ways to connect — API or web portal. Both give full access to your workflows, quality data, and outputs.
Cophy.API
REST API
A REST API that fits into your existing systems — CRM, ERP, telephony, or internal tooling. Send data in, get structured JSON back. Fastest path to production for teams with engineering resources.
- RESTful JSON API — structured output per configured workflow
- API key management and per-tenant isolation
- Usage monitoring and cost tracking built in
- Async queue processing for high-volume workloads
Client Portal
Full Web Application
A complete web application for teams that need visibility, configuration, and management tools — regardless of whether they also use the API. Manage workflows, review outputs, run quality experiments without engineering involvement.
- Per-tenant data isolation
- Role-based access controls
- Full audit trail and reporting
Processing Pipeline
From raw input to structured output — in a controlled, observable pipeline.
Step 1 — Input
Data enters via API call or direct upload — call recordings, transcripts, documents, images, or structured records. Each request is tagged, queued, and routed based on workflow configuration.
Step 2 — PII Obfuscation
Before any data reaches an AI model, configured rules detect and replace SSNs, account numbers, names, phone numbers, and custom identifiers. Nothing sensitive reaches the LLM unprotected.
Step 3 — RAG Context
The workflow pulls relevant context from the Learning Center — the right policy documents and guidelines for this specific request. Re-ranking rules control which content surfaces and in what priority order.
Step 4 — AI Processing
The configured workflow runs against your chosen model. Multi-step workflows chain outputs from one component into inputs for the next. Each step has configurable prompts, structured output schemas, and retry logic.
Step 5 — Structured Output
Results come back as structured JSON — scorecards, summaries, classifications, flags, extracted fields. Delivered via API or visible in the portal.
Step 6 — Quality Tracking
Every output is stored against the input. Quality experiments re-run any dataset against updated prompts. Human reviewers label outputs. Precision and recall are calculated automatically per field.
The Quality Loop
AI that improves itself — with human oversight, not instead of it.
Most platforms treat AI quality as a launch-day concern. CophyAI treats it as an ongoing operational process — the same way you'd manage quality in any other part of your business.
1. Run
Process real data through configured workflows.
2. Review
Users flag incorrect outputs. Human labelers mark TP/TN/FP/FN.
3. Measure
Precision, recall, F1 calculated per output field. Quality thresholds trigger alerts.
4. Improve
Discrepancy patterns surface prompt improvement candidates.
5. Experiment
New prompt versions tested against labeled datasets before deployment.
6. Deploy
Better versions promoted to production. Old versions retained for rollback.
Knowledge Layer
AI that knows your business — and keeps up when it changes.
Prompts alone are not enough. AI needs to understand your policies, products, and processes — and that knowledge changes constantly. The Learning Center is where you maintain that knowledge without engineers involved every time a policy updates.
| Component | Detail |
|---|---|
| Vector Database | Client-specific, fully isolated per tenant |
| Embedding Model | text-embedding-3-large (text-embedding-3-small available for cost optimization) |
| Re-ranking | Configurable by authority level, recency, department affinity, or custom logic |
| Versioning | Every document upload creates a new version; previous versions retained |
| Supported Formats | PDF, DOCX, TXT, CSV |
| Scale | Bulk upload with async queue; hundreds of documents without manual intervention |
Agent Architecture
AI agents that reason, act, and stay within guardrails.
CophyAI Agents go beyond chat. They use the ReAct reasoning strategy — observe, think, act — to complete multi-step tasks using the tools and knowledge you configure. Every action runs through a risk framework you define.
Risk Levels Per Action
Read Only
Agent retrieves and summarizes information. No writes, no external calls, no actions with consequences.
Draft Action
Agent proposes an action — creates a draft, suggests an update, prepares a message. A human reviews and approves before anything is committed.
Committed Action
Agent executes directly. Reserved for fully trusted, low-risk, well-defined operations. Requires explicit configuration and human sign-off during setup.
Built-in Safety Layers
- Prompt injection detection — guards against manipulative inputs
- RAG injection watch — monitors retrieval results for adversarial content
- Confabulation limits — agent stops and flags rather than invents answers
- Max iteration caps — prevents runaway loops
| Property | Detail |
|---|---|
| Reasoning Strategy | ReAct (Reasoning + Acting) |
| Loop Control | Configurable — agent decides when to terminate, or max iterations set |
| Tool Integrations | MCP (Model Context Protocol) servers for external system access |
| Agent Personas | Custom identity, tone, domain focus, and system prompt per agent |
| Model Provider | Azure OpenAI (GPT-5-mini, GPT-5.3-chat); architecture supports additional providers |
Security Architecture
Data control at every layer.
CophyAI is designed for regulated environments where data handling isn't optional — it's a requirement.
| Layer | How It Works |
|---|---|
| Obfuscation | Presidio NLP-based entity detection + custom regex pattern rules. Runs before any LLM call. |
| Tenant Isolation | Complete data separation — no cross-tenant data access at any layer. Dedicated vector DB per tenant. |
| Selective Storage | Configure exactly what gets retained. Nothing stored without explicit policy. |
| Audit Trail | Full request/response logging; every obfuscation event recorded; user actions logged. |
| Model Access | API keys managed per model deployment; usage monitored per tenant. |
Deployment & Integration
Built to fit into your existing stack — not replace it.
What CophyAI is NOT
- Not a CRM — we enrich CRM data, we don't replace it
- Not a telephony platform — we process calls, we don't route them
- Not a compliance system of record — we surface compliance risks, we don't manage violations
- Not a training LMS — we generate coaching content, we don't manage learning curricula
What CophyAI Connects To
- Telephony platforms — call recordings via API
- CRM systems — structured output delivery
- Document management systems — Learning Center ingestion
- External APIs via MCP tool integrations for agents
- Azure OpenAI (current model provider; architecture supports additional providers)
