DEPLOYPROAI ENGINEERING · AI TRANSFORMATION · DATABASE
The database workyour AI roadmap depends on.
Vector and hybrid search, schema design for agent workloads, legacy migrations and the performance work that keeps AI features fast under real load. Inside your cloud, on the engines you already run.
- Vector & hybrid search
- Schema design for agents
- Legacy migrations
- Performance & cost
- Access control
- Backup & recovery
The problem we solve
Agents write to systems that were designed for people typing one record at a time.
- Vector search bolted on to a production database slows the queries that pay the bills.
- A legacy migration scoped at three quarters quietly freezes every feature that depends on it.
- Row-level access was never modelled because a person always sat in front of the screen.
- Nobody has tested the restore.
Step 1
Understand
Schemas, access patterns, load profile and the queries that matter to the business.
Step 2
Design
Search indexes, schema changes and access rules sized for agent traffic, not human traffic.
Step 3
Migrate safely
Scripts with rollback, run first against a copy, then in a window you choose.
Step 4
Measure under load
Latency, throughput and cost at the volume the agents will actually generate.
What we build
What we build.
Vector and hybrid search
Embeddings, indexes and hybrid ranking on your existing engine where it holds up, and a dedicated store only where the numbers say so.
Schemas for agent workloads
Tables, queues and idempotency designed for high-volume, retry-prone machine writers.
Legacy migrations
Service extraction and engine moves with the working pattern behind a three-quarter migration finished in two days.
Performance and cost
Query plans, indexes and caching tuned against the load profile, with the bill tracked alongside the latency.
Access control
Row and column rules so an agent sees exactly what the person it acts for would see.
Backup and recovery
A tested restore, a written runbook and a recovery drill you have actually run.
What you walk away with
Deliverables. Not promises.
A schema and migration plan with rollback
A search index with recall measured on your data
Migration scripts, run first on a copy
A load-test report at agent volume
An access-control model, documented
A tested recovery runbook
Production proof
Evidence before adjectives.
Three quarters to two days
A PHP-to-Go migration planned for three quarters completed in two days, and engineering capacity was freed for product work.
A $7.5B FinTech platform
34 weeks to one week
Lead time from requirements to production now runs one week. It was 34 weeks. The engineering team was trained to sustain the workflow independently.
India's leading credit-ratings provider
Delivered by Ashish Tripathi in prior enterprise engagements, before DeployProAI was founded. Full detail under NDA.
FAQs
Frequently asked questions
The ones you already run. Relational, document and search engines on your cloud or on-premise. We do not introduce a new engine unless the load figures say the existing one cannot carry the workload.
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.
You do. Everything DeployProAI produces for you during an engagement belongs to you, including the code, the documentation and the plans. SupportSight and Rehearse, two of DeployProAI’s Agentic Accelerators, are provided under an annual licence.
Start with the outcome.
Tell us which system the agents will read from and write to. We will tell you whether it is ready and what it would take.
- NDA-first
- Your codebase
- Measured with DORA and SPACE
- GDPR
- DPDP
- SOC 2 practices
- PCI-DSS
- IRDAI-aware
- EU AI Act