Skip to main content
XentraSol — Where Innovation Meets Excellence
AI & Data

AI Development

LLM copilots, RAG assistants, agents, and document intelligence built on your own data.

Overview

What AI Development means at XentraSol

We build AI products that survive contact with production. That means grounded retrieval over your own data, evaluation harnesses that catch regressions, cost controls, and human-in-the-loop steps wherever a wrong answer would be expensive.

Most engagements start narrow: one workflow, one dataset, measurable before-and-after numbers. Once value is proven we widen the surface area — more sources, more automation, more autonomy — without rewriting the foundation.

Typical engagement

Discovery
1–2 weeks
First release
4–8 weeks
Team
Senior specialists only
Ownership
100% yours
Business benefits

Why teams invest in this

Hours back per employee

Research, classification, and drafting work that took hours is reduced to minutes with an auditable trail.

Answers grounded in your data

Retrieval-augmented architecture means responses cite your documents instead of hallucinating.

Measurable ROI

We instrument accuracy, handling time, and deflection rate from day one so value is provable.

Enterprise-safe by default

Access control, PII handling, prompt-injection defences, and full logging on every call.

Scope

What's included

Custom GPT copilots and chat assistants
Retrieval-augmented (RAG) search over private data
AI agents and multi-step workflow automation
Document, OCR, and extraction pipelines
Model evaluation, guardrails, and red-teaming
Fine-tuning and prompt optimisation
Vector database design and cost tuning
Technology

The stack we build on

Models

OpenAI GPTAnthropic ClaudeGoogle GeminiLlamaMistral

Frameworks

LangChainLlamaIndexVercel AI SDKPyTorchHugging Face

Data

pgvectorPineconeWeaviateQdrantElasticsearch

Platform

PythonTypeScriptFastAPIAWS BedrockAzure OpenAI
Process

How we deliver

01

Discovery

We map the current state, constraints, and success metrics with your stakeholders before a line of code is written.

02

Planning

Scope, architecture, delivery plan, and dates are agreed in writing so everyone knows what ships when.

03

Design

Flows, wireframes, and UI are validated with real users and signed off before build.

04

Development

Weekly increments with a live demo at the end of each one, code review, and automated tests throughout.

05

Testing

Functional, regression, performance, and security testing on every release candidate.

06

Deployment

Automated CI/CD releases, monitoring, and a controlled go-live with rollback ready.

07

Support

Warranty period after launch, then an optional retainer for monitoring, patching, and improvements.

Proof

Related case studies

Finance & Fintech

Hive Tax AI

An AI tax assistant that cut research time for accounting teams

Less time spent on routine research
70%Less time spent on routine research
More client queries handled per day
3xMore client queries handled per day
AI answers returned with source citations
100%AI answers returned with source citations
Read case study
FAQ

Questions about AI Development

No. We use enterprise API tiers with training disabled, or self-hosted models where data residency requires it.

Ready to start your ai development project?

Book a free consultation and we'll give you a straight answer on scope, timeline, and cost.