Begin a conversation
ServicesAI ConsultingProductsBlogAboutContact
Get in touchBegin a conversation
AI Consulting

AI engineering,
ready for production.

LLM applications, AI agents and copilots, RAG-based knowledge systems, computer vision pipelines, and AI integration into existing products. Built and operated by the same senior team that ships our cloud and software work.

LLM Applications

Chat, agents, copilots, on your stack.

Production LLM applications grounded in your data, your tools, your domain. Streaming UIs, structured outputs, tool calling, function routing, and the eval harness that keeps quality from drifting.

Discuss this work
  • Chat & assistants
    Domain-tuned LLM assistants over your docs, tickets, and data.
  • Structured outputs
    Schema-validated responses for downstream automation.
  • Tool use & routing
    Models that call your APIs, your way.
  • Streaming UX
    Token-by-token UIs that feel instant, not laggy.
AI Agents & Copilots

Agents that do the work, not the demo.

Multi-step agents for ops, support, sales research, and internal workflows. Scoped tool access, deterministic guardrails, and observability you can debug. Built to ship, not to impress on a slide.

Discuss this work
  • Workflow agents
    Ops, support, sales-research; bounded scope, audited actions.
  • Internal copilots
    Productivity surfaces for support, finance, and engineering teams.
  • Guardrails
    Permissioned tool use, output validation, and human-in-the-loop gates.
  • Observability
    Traces, replays, eval suites; debug agents like real software.
RAG Systems

Retrieval that actually retrieves.

Production-grade retrieval-augmented generation. Ingestion pipelines, embedding strategies, hybrid search, re-ranking, and the eval discipline that tells you when retrieval breaks before users do.

Discuss this work
  • Ingestion pipelines
    Connectors, chunking strategies, structure preservation.
  • Hybrid search
    Vector + keyword + filters, tuned to your corpus.
  • Re-ranking
    Quality lift on the top-K, where it counts.
  • Retrieval evals
    Regression tests on recall, precision, faithfulness.
Computer Vision & OCR

Pixels to structured data.

Document processing, image classification, layout analysis, and screen-aware copilots. Built on managed vision APIs or self-hosted models, depending on cost, latency, and privacy constraints.

Discuss this work
  • Document OCR
    Invoices, forms, contracts; structured outputs, audit trails.
  • Layout analysis
    Tables, figures, regions; preserve meaning, not just text.
  • Image classification
    Task-tuned models with eval harnesses you can rerun.
  • Screen-aware copilots
    Vision models that understand the UI they look at.
AI Integration

AI into systems that already work.

We add AI capability to existing products without rewriting them. APIs, side-cars, event hooks, and migration paths that let your team adopt AI incrementally, then scale what works.

Discuss this work
  • Capability audit
    Where AI earns its place in your current product surface.
  • Incremental rollout
    Feature-flagged, instrumented, reversible.
  • Cost & latency budgets
    Model choice driven by economics, not hype.
  • Eval-first delivery
    Quality gates before the feature ships, not after.
How we work

Discovery to production,
on the same playbook.

AI work runs on the same engineering discipline as the rest of our practice. The difference is the eval suite, which we treat as the source of truth, not the demo.

Discovery

Use-case framing, data audit, evaluation rubric agreed in writing.

Prototype

Two-week spike with a working slice, eval scaffold, and cost projection.

Evals

Regression suite covering retrieval, generation, and downstream business metrics.

Integration

Feature-flagged rollout into the surfaces your users actually touch.

Operate

SLOs, drift monitoring, cost reviews, and continuous eval against the suite.

Documented exits

If we leave, your team owns the system. Runbooks, ADRs, dashboards included.

The stack

Model choice driven
by economics, not hype.

We pick the model, vector store, and orchestration framework that fits the cost, latency, and privacy profile, not the one trending this week.

Model providers

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • Open-weight models (Llama, Mistral, Qwen)

Data ingestion

  • Unstructured.io
  • LlamaParse
  • Custom ETL pipelines
  • Airflow / Prefect

Vector and search

  • pgvector (Postgres)
  • Pinecone
  • Weaviate
  • Elasticsearch hybrid

Frameworks

  • LangChain
  • LlamaIndex
  • Vercel AI SDK
  • Custom orchestration where it pays off

Evaluation

  • Ragas
  • Custom eval suites
  • LLM-as-judge with calibration
  • Human-in-the-loop review

Safety & guardrails

  • OpenAI Moderation
  • Lakera Guard
  • NeMo Guardrails
  • Schema-validated outputs

Hosting and ops

  • AWS Bedrock
  • Azure OpenAI
  • Google Vertex AI
  • Self-hosted on Kubernetes

Observability

  • Langfuse
  • LangSmith
  • OpenTelemetry traces
  • Custom dashboards in Grafana
Engagement models

Three ways to start.
One senior bench.

AI Sprint

Validate the idea

A focused engagement to take an AI use case from idea to working prototype, with evals and a production roadmap. Best when you need to validate before committing to a full build.

Build & Operate

Ship and run

End-to-end build of an AI capability, integrated into your product, with our team operating it through launch. Senior engineers from day one to production.

Senior on Demand

Depth on your team

A senior AI engineer joins your team, with our wider engineering bench on call. Best when you have momentum but need depth on agents, RAG, or evals.

Begin a conversation

Bring us the
AI problem.

sales@xpanso.comStart an AI sprint
Begin a conversation