DEPLOYPROAI ENGINEERING · AI TRANSFORMATION · DATA
Your data, made readyfor AI to use.
Retrieval pipelines, document ingestion and the data contracts that feed agents and LLM features, built in your environment, on your data, with lineage you can show an auditor.
- Retrieval pipelines
- Document ingestion
- Data contracts
- Lineage & audit
- Quality gates
- Privacy controls
The problem we solve
Most AI failures we see are data failures. The model was fine. The retrieval was not.
- Documents arrive as scans, spreadsheets and email threads, not as tables.
- Nobody can say which source a given answer came from, so nobody can sign off on it.
- Personal data flows into prompts with no record of it having done so.
- The pilot worked on a hand-picked folder and stalled on the real archive.
Step 1
Understand the sources
Every system, file store and mailbox the process touches, who owns it, and what policy governs it.
Step 2
Build the pipeline
Ingestion, cleaning, chunking and retrieval in your repository, on your infrastructure.
Step 3
Gate the quality
An evaluation set with recall and precision measured before the pipeline feeds anything.
Step 4
Prove the lineage
Every answer traceable to its source, every personal-data touch logged.
What we build
What we build.
Retrieval pipelines
Chunking, embedding, hybrid search and re-ranking tuned on your documents, with recall measured rather than assumed.
Document ingestion
Scans, PDFs, spreadsheets and email threads turned into structured records with the original kept alongside.
Data contracts
What each feed promises, in writing and in tests, so an upstream change breaks a build and not a customer.
Evaluation datasets
A held-out set drawn from your real data, so retrieval quality is a number you can watch over time.
Lineage and audit logs
Which source fed which answer, and which person saw it, in a log a regulator will accept.
Privacy controls
Redaction, retention and access rules designed for DPDP and GDPR, applied before the data reaches a model.
What you walk away with
Deliverables. Not promises.
The pipeline, running in your repository and your cloud
A source registry with owners and policies
A retrieval evaluation set with the scores
Data contracts with tests
A lineage log you can show an auditor
A privacy review against DPDP and GDPR
Production proof
Evidence before adjectives.
In production
SupportSight reads shared and personal mailboxes inside the environment, works out who owns each customer thread and how long it has waited, and flags it before the reply deadline. It never stores email bodies.
50% to 80.1%
Call quality on one insurance client's line rose from 50% to 80.1% over three quarters, on an AI-native quality platform now in production.
FAQs
Frequently asked questions
No. DeployProAI works within your own code repositories, systems and cloud accounts, and follows your security policies. DeployProAI’s Agentic Accelerators are installed in your own cloud account or on your own servers. During training, teams use the AI tools your organisation has approved, and any data is anonymised where required.
Yes. With DeployProAI, you choose the AI model. It can be a hosted model, or one that runs entirely on your own servers, in which case no data leaves your organisation. SupportSight can run entirely on models hosted on your own servers. Rehearse processes calls in India, on AWS in Mumbai and on Sarvam.
No. We start from the sources you already have and build the smallest pipeline that serves the process in scope. If the assessment shows a platform is needed, we say so with the figures.
Start with the outcome.
Tell us which documents, mailboxes or systems the process lives in. We will tell you what it takes to make them safe for AI to read.
- NDA-first
- Your codebase
- Measured with DORA and SPACE
- GDPR
- DPDP
- SOC 2 practices
- PCI-DSS
- IRDAI-aware
- EU AI Act