All services
01 — AI Engineering

AI Software Development

Vendor-neutral AI, built for production.

Overview

Vendor-neutral AI, built for production.

Dexra builds production-grade AI applications that are model-agnostic by design. We don't lock clients into a single vendor — we architect around the best model for each job, and re-architect as the landscape shifts.

AI Software Development
What we deliver

Capabilities under this service.

  • Custom LLM application development (chat, copilot, agentic products)
  • RAG pipelines and vector database architecture (Pinecone, Weaviate, pgvector, Qdrant)
  • AI agent and multi-agent orchestration with tool-use and function calling
  • Model evaluation, benchmarking and cost/performance optimisation across providers
  • Prompt engineering, fine-tuning and RLHF-style evaluation pipelines
  • LLM gateway / routing layers for automatic failover between providers
  • AI cost governance and usage observability across LLM vendors
  • Responsible AI: guardrails, content moderation, red-teaming and compliance docs
  • Legacy application modernisation with embedded AI features
Coverage

LLM & Model Coverage

Current, hands-on experience across every generation and vendor — not a marketing slide.

Dexra logo
OpenAI

GPT-4o, GPT-4.1, o-series reasoning, Assistants, fine-tuning

Anthropic

Claude Opus, Sonnet, Haiku + Claude Code agentic tools

Google

Gemini 2.x Pro & Flash, Vertex AI deployment

Meta

Llama open-weight models, on-prem / self-hosted fine-tuning

Mistral

Mistral Large & Small, open-weight deployments

xAI

Grok API integration

Cohere & Bedrock

Amazon Bedrock multi-model gateway

Open-Source

Ollama, vLLM, TensorRT-LLM for local inference

Case studies

Outcomes we've delivered.

A selection of engagements under ai software development. Client names anonymised where required by NDA; metrics are measured post-deployment.

Discuss your project
Case 01London, UK

Tier-1 UK Retail Bank

Financial Services
Challenge

Fragmented customer-service knowledge across 40+ internal systems drove 6-minute average handling times and inconsistent answers from 1,800 agents.

What we did

Deployed a multi-LLM RAG assistant routing between Claude Sonnet (reasoning) and GPT-4o (drafting), with pgvector over 2.1M policy documents and full audit logging for FCA compliance.

42%
shorter handling time
94%
answer accuracy (blind-graded)
£3.8M
annualised opex saved
Case 02Amsterdam, NL

European MedTech Scale-up

Healthcare / Life Sciences
Challenge

Radiology reporting backlog of 11 days was blocking clinical trials; single-vendor lock-in on an early GPT-3.5 workflow created compliance and cost risk.

What we did

Rebuilt as a vendor-neutral pipeline (Gemini 2 Pro for vision, Llama 3.1 70B self-hosted on H100 for PHI-sensitive text) with automatic model-routing and human-in-the-loop review.

11 → 2 days
report turnaround
71%
inference cost reduction
ISO 13485
audit passed first cycle
Case 03New York & London

Global Legal SaaS

Legal Technology
Challenge

Contract-review copilot hallucinated on complex clauses and lacked evaluation infrastructure to safely release model upgrades.

What we did

Introduced an eval harness with 4,600 graded contracts, guardrails via Anthropic + custom classifiers, and a routing layer that A/B tests new models against the incumbent in production.

0.4%
hallucination rate (from 6.1%)
3.2x
faster model release cadence
$1.2M
ARR unlocked in 6 months
FAQ

AI questions, answered.

Scope, timelines, security posture and delivery models — the details buyers and technical leads ask us before signing.

Ask a specific question
What's in scope for a typical AI engagement?

Discovery of the target outcome and data landscape, model selection across OpenAI, Anthropic, Google, Meta, Mistral and xAI, RAG or agent architecture, evals, guardrails, production deployment and post-launch tuning. We scope in one-week increments and share a fixed statement of work before build starts.

How long does a first production release take?

A focused pilot with real users is typically 6–10 weeks. A production-grade multi-LLM application with evals, observability and compliance sign-off usually lands in 12–18 weeks depending on data readiness and regulatory scope.

How do you handle security, IP and data residency?

We default to zero data retention with commercial providers, support fully self-hosted Llama, Mistral and Qwen deployments on your own GPUs, and honour EU/UK data residency. Prompts, embeddings and outputs stay inside your tenancy; we sign DPAs and support ISO 27001, SOC 2 and FCA/PRA controls.

Which delivery models do you offer?

Fixed-scope builds, embedded squads inside your engineering org, or an outcome-based managed service where we own the roadmap, evals and on-call. Every model includes a named delivery lead and weekly written status.

Explore more

Other services

Ready to talk ai software development?

Tell us the outcome you're aiming for. We'll come back with the architecture and delivery plan inside one business day.