ADI Platform
AI agents that run regulated operations.
In production at tier 1 institutions across LatAm, in banking and healthcare. Voice, WhatsApp and web on one platform.
ADI Agents
Five agents covering origination, onboarding, transactions, collections and support.
See the agents →ADI Platform
Our own voice stack, guardrails in code, and deployment wherever your policy requires.
See the platform →Use cases
Banks, BPOs, fintechs, insurers and healthcare. What blocks each operation.
See the cases →The blocker
Why the AI project stalled before production.
Auditability
Risk will not approve what it cannot reconstruct. With no per-turn trail, the pilot stays in the sandbox.
PII
Personal data leaving for a model provider is an immediate veto from security.
Hallucination
An instruction in a prompt is not control. The agent can still say what it must not say.
Private network
Policy requires media and inference to run inside the perimeter, not on shared public cloud.
Control
Guardrails in code, verified outside the model.
Deterministic filters run before and after the LLM. What the agent can do comes from a closed action catalogue, with enums validated outside the model.
Every behaviour change goes through versioned regression QA, with gradual canary and immediate rollback.
Mandatory
Cannot be disabledPrompt injection
PII exfiltration
Crisis signals
Configurable
Eight filters, three severity levels, including strict topical focus.
Coverage
One compliance approval runs the whole cycle.
All four stages run on the same base. Approval happens once, at the platform level.
Start with one stage and expand. None of them requires buying the others.
Results in production
Time
Weeks, not quarters.
1 to 3 weeks
Full onboarding, from scope to the first agent in production.
2 to 5 days
Voice connected to your SIP trunk, with TLS and SRTP.
Day 1
Sandbox open for your team to test with synthetic data.
Integration sits inside the contract scope. It is not billed as a separate consulting project.
Bring your Risk team.
The conversation that usually stalls the project is the first one we have.
What you bring
Your compliance checklist
One flow, not five
Your SIP trunk and your systems
What we bring
A dedicated pod: PO, AI engineers and a compliance liaison
Sandbox with synthetic data on day one
Guardrails configured for your domain
What comes out
One agent in production, auditable per turn
Full trail: what it said, why, on which version
Operational metrics measured, not estimated