Production-grade AI from pilot to scale.
We design, build, and operate AI systems that actually ship, LLM-powered agents, retrieval and search, computer vision, classical ML, evaluation harnesses, and the eval-driven engineering loop that keeps them honest in production.
The actual stack, eval-gated, observed, governed.
Production AI is 90% the supporting infrastructure. Here's every tool we ship with by default, plus the evals and observability that hold the model accountable.
Anthropic Claude
OpenAI GPT-4/5
Llama 3
Mistral
Gemini
Cohere
ElevenLabs
LangChain
LlamaIndex
Vercel AI SDK
FastAPI
TypeScript
Python
Pinecone
Weaviate
pgvector
Qdrant
Datadog LLM
Weights & Biases
MLflow
SageMaker
Modal
Replicate
Vertex AIThe gap isn't AI ideas. It's AI that actually runs.
Most enterprises have no shortage of AI roadmaps and pilot decks. The hard part is the second 90% of the work, the evals, the guardrails, the data pipelines, the cost discipline, and the ongoing optimisation that turns a Friday prototype into a system the business can trust on Monday.
- Production-first engineeringBuilt with evals, observability, and rollback plans from day one. We don't bolt operations on at the end.
- Toolchain-agnosticOpenAI, Anthropic, open weights, your private models. We pick what fits your data, latency, and cost, not what's on a partner list.
- Outcome-anchored scopeEvery engagement defines a business outcome before the first line of code. We measure ourselves against it.
- Build → run → optimiseThe team that ships the system can stay to run and optimise it. No knowledge transfer, no cold handoff to operations.
Six capability areas. One delivery model.
Each capability ships as a productionised solution with evals, observability, and a defined operating model handed over to your team.
AI agents & agentic workflows
Autonomous and semi-autonomous agents for multi-step business processes, research, triage, document workflows, customer ops.
- Tool-using agents with structured outputs
- Human-in-the-loop checkpoints & approvals
- Multi-agent orchestration & supervision
- Production tracing, evals, and rollbacks
LLM integration, fine-tuning & RAG
Domain-specific LLM deployment with retrieval-augmented generation, fine-tuning, and the data pipelines to feed both reliably.
- RAG over your documents, tickets, codebase
- Fine-tuning on proprietary data
- Model routing & cost-tier optimisation
- Hallucination & citation guardrails
Speech intelligence & NLP
Voice interfaces, transcription, sentiment analysis, and intelligent document processing.
- Real-time transcription & diarisation
- Voice agents & IVR replacement
- Document intelligence & extraction
- Sentiment, intent, and entity tagging
Intelligent process automation
Replacing manual processes with adaptive, learning automation that improves with usage, not brittle if-this-then-that scripts.
- Back-office workflow automation
- Underwriting, KYC, and triage assistants
- Approval & review co-pilots
- Continuous improvement loops
Cloud-native AI architecture
Microservices, event streams, and infrastructure designed for AI workloads, scale, latency, and cost discipline built in.
- Inference layer & model serving
- Vector stores, embeddings, retrieval
- Cost & latency observability
- FinOps for AI workloads
Eval-driven engineering & MLOps
The discipline that separates demoware from production. Every release is evaluated and reversible.
- Eval suites shipped with every model
- Drift & performance monitoring
- Red-team & safety harnesses
- Versioning, rollback, and replay
From pilot to production.
Three engagement shapes, sized to where you are on the AI adoption curve.
4-6 week AI pilot
A fixed-scope, fixed-price pilot that proves the highest-impact use case before committing to a full programme.
- Eval-gated prototype
- Production-ready architecture
- Costed expansion plan included
- 30-day post-launch hyper-care
10-14 week production build
Full production deployment with evals, observability, and operating model handoff. The system runs after we leave.
- Production-grade deployment
- Eval suites & observability
- Operating model handoff
- Optional ongoing operations
Embedded AI team
An ongoing AI engineering pod inside your team, eval-driven, accountable, monthly cadence.
- Senior AI engineers embedded
- Continuous experimentation & ship
- Quarterly programme reviews
- Cross-practice handoffs
Four phases. Eval-gated.
Discovery → Definition → Delivery → Optimise. Each gate ships with a signed decision document.
Discovery
Use-case shortlist, data audit, success-metric definition, and a costed pilot proposal in 48 hours.
Definition
Architecture, eval plan, and risk register. Signed off before the first model call.
Delivery
Prototype shipped with full eval suite. Weekly demos, real artefacts, never summary slides.
Optimise
Production deployment, observability, drift monitoring, and the second-order optimisations that compound.
What we commit to, in writing.
We commit to outcomes in writing before the build starts.
Where AI Solutions connects.
Most AI engagements span beyond a single practice. Here's how the rest of Xperion plugs in.
Have an AI use case in mind?
One scoping call. Within 48 hours of the call, you get a costed plan with the senior partner who'll lead the build, including the eval and production path.