Autonomous intelligence, deployed.
Ship AI agents that reason, act, and learn across support, sales, voice, and operations.
One orchestrator. A fleet of specialists.
CapraZone agents don't run alone. A supervisor routes every task to the specialist best suited to it, streams tool calls in parallel, and keeps a full audit trace. Tap a node to inspect a live run.
Design your agent. Watch it run.
Pick an industry, a job, and a tone. CapraZone Lab assembles a model-agnostic agent persona, tool stack, and a live simulated trace — so you can see what production-grade autonomy looks like before writing a line of code.
Higher autonomy means fewer human checkpoints before action.
Configure the agent on the left and hit Generate to see a live simulated run.
Eight surfaces, one intelligence layer.
Every CapraZone agent is composed from the same substrate — a shared reasoning core, memory, and tool graph — then specialised for the surface it lives on. No brittle prompt kits; production-grade systems from day one.
Customer Support AI
24/7 tier-1 resolution across chat, email, and voice.
Sales AI
Prospecting, qualification, and follow-through at machine speed.
Voice AI
Human-quality voice for inbound and outbound calls.
Knowledge AI
Retrieval-augmented answers grounded in your data.
WhatsApp AI
Conversational commerce on the channels customers use.
Email AI
Draft, triage, and reply with your team's tone and rules.
Internal AI
Copilots for finance, HR, and ops teams.
Operations AI
Agentic workflows that execute across your stack.
A cognitive loop, engineered for production.
Perceive → reason → act → learn. Every phase is instrumented, evaluated, and swappable.
Perceive
Multi-modal ingestion from CRM events, tickets, voice, documents, and telemetry — normalised into a unified context graph.
Reason
Task-specific reasoning chains combine LLMs, tools, and business rules with guardrails, evaluations, and cost controls.
Act
First-class connectors execute writes against Salesforce, HubSpot, Slack, ERPs, ticketing, calendars, payment rails, and databases.
Learn
Every outcome is scored and stored. Prompts, tools, and routing policies auto-tune from real production traffic.
Where teams deploy CapraZone agents first.
Autonomous Support Desk
Deflects 60–80% of tier-1 volume with grounded answers, escalates cleanly with full context handoff.
Outbound SDR Fleet
Researches accounts, personalises multi-touch sequences, books meetings, and updates CRM in real time.
Voice Concierge
Sub-500ms latency phone agents that qualify, schedule, and take payment — indistinguishable from staff.
Ops Copilot
Chat-native workflows for finance close, HR onboarding, and procurement approvals across your stack.
Document Intelligence
Extracts, validates, and routes contracts, invoices, and clinical notes with human-in-the-loop review.
Knowledge Base Agent
Live-syncs with Notion, Confluence, Drive, and Zendesk — always answers from the latest source of truth.
Model-agnostic. Tool-obsessed.
We pick the right model for each hop and route around outages, price shifts, and quality drift automatically.
- OpenAI GPT-5
- Anthropic Claude 4
- Gemini 2.5
- Open-weights (Llama, Qwen, Mixtral)
- Pinecone
- Weaviate
- pgvector
- Turbopuffer
- LangGraph
- Temporal
- Inngest
- Custom agent runtime
- Langfuse
- Braintrust
- OpenTelemetry
- Custom eval harness
From baseline to autonomy in six weeks.
Agents fail in production for boring reasons: no baseline, no evals, no rollback. Our rollout is designed around those three failure modes.
- Week 0–1Step 01
Opportunity mapping
We shadow the workflow, instrument the current baseline (volume, handling time, cost per resolution, error rate), and rank candidate agents by value-over-effort. You get a scored backlog, not a slide deck.
- Week 1–3Step 02
Grounding & tooling
Knowledge sources are chunked, deduped, and evaluated for retrieval quality. Every write action becomes a typed tool with schema validation, idempotency keys, and rollback semantics.
- Week 3–5Step 03
Shadow mode
The agent runs on live traffic but ships nothing. Its proposed actions are diffed against what humans actually did, producing an honest accuracy curve before a single customer is exposed.
- Week 5–6Step 04
Supervised autonomy
Low-risk intents go fully autonomous; higher-risk ones queue for one-click human approval. Confidence thresholds are tuned per intent rather than globally.
- Week 6+Step 05
Compounding
Weekly eval runs, regression suites on golden traces, prompt/tool/routing updates, and monthly business reviews tied to the original baseline metrics.
Autonomy you can actually sign off on.
Six control planes sit between the model and your business systems. None of them are optional.
Deterministic tool contracts
Models never touch your systems directly. Every side effect passes through a typed tool with validation, rate limits, and an audit record.
Grounding enforcement
Answers must cite retrieved context. Unsupported claims are blocked or downgraded to a clarifying question instead of a hallucination.
PII handling
Redaction at ingestion, field-level encryption at rest, configurable retention windows, and zero training on your data by default.
Escalation policy
Sentiment, risk keywords, repeat contacts, and low confidence all trigger handoff with a full context packet — never a cold transfer.
Continuous evaluation
Golden datasets, LLM-as-judge scoring, and regression gates in CI. No prompt reaches production without passing the suite.
Cost governance
Per-intent token budgets, model routing by difficulty, aggressive caching, and alerting when unit economics drift.
What changes on the P&L.
Questions we get in every first call.
›How is this different from a chatbot?
A chatbot answers. An agent acts. CapraZone agents call typed tools against your CRM, ERP, ticketing, and payment systems, then verify the result — with every step traced, evaluated, and reversible.
›What happens when the agent is unsure?
Confidence thresholds are tuned per intent. Below threshold the agent either asks a clarifying question or escalates to a human with a full context packet including retrieved sources and its proposed action.
›Do you train models on our data?
No. Your data is used for retrieval and evaluation only, with redaction at ingestion and configurable retention. Fine-tuning happens only on request, on infrastructure you approve.
›Which model do you use?
Whichever wins the eval for that hop. We route per task across frontier and open-weight models, and fail over automatically on outages, price shifts, or quality drift.
›How long until we see impact?
Shadow mode typically produces a credible accuracy curve inside three to five weeks, with measurable deflection or throughput gains in the first quarter of production traffic.
Stop shipping bots. Ship systems.
Book a 30-minute discovery. We'll map three high-leverage agent deployments for your business.
Book discovery →