ARCHITECTURE & APPROACH
The intelligent layer between you, the LLM and your data.
Dafin sits at the centre of your data infrastructure — interpreting questions with a language model, calculating answers from your verified sources, and returning them with full reasoning and citations.
01 · How it works
Four stages. One promise: no invented answers.
02 · Calculated, not generated
Why Dafin doesn’t hallucinate.
Hallucinations happen when a language model generates content not grounded in real data. Dafin’s architecture prevents this by structurally separating reasoning from generation.
Answers are calculated from your verified data sources, with the LLM used only to interpret the question and format the response. If the data needed is unavailable, Dafin says so.
D
No data? No answer.
honest by design
What was the Müller portfolio return for January 2019?
I don’t have position data for that date range. The earliest data available for this client begins 14 March 2020. Would you like me to summarise from there?
HONEST BY DESIGN
No invented numbers. No plausible fiction.
03 · Open connector model
Plugs into the systems your firm already runs.
No data migration. No model training. Just connect what you have.
PMS Database
Market Data
KYC Data
Documents
Internal Data
Cloud Storage
Analytics APIs
CRM Systems
Compliance Docs
Custodian Feeds
Risk Models
Order Management
04 · Implementation
Production-ready in months, not years.
Most deployments go live within 3–4 months — fast for a regulated wealth platform, where comparable rollouts take 12 to 24 months.
Week 1
Discovery
Map your data sources and define the success criteria.
Weeks 2–6
Connectors
Plug Dafin into your PMS, custodian feeds and document stores.
Weeks 7–10
Security review
Your team validates architecture, runs pen tests, signs off.
Weeks 11–16
Rollout
Onboarding by role. First users active in month two.
LIVE DEMO · 20 MINUTES
See it run on your data.
A focused demo with our solutions team — we’ll walk through architecture, connectors and what your typical deployment would look like.

