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Generative AI consulting

Generative AI Consulting

Production-focused generative AI consulting for companies building AI agents, intelligent document systems and custom LLM/SLM solutions. We help teams move from AI ideas and prototypes to reliable production systems, with the engineering depth to handle evaluation, security, performance and cost. Our strongest application areas are AI agents and document intelligence, where there are clear opportunities to automate workflows and improve operational efficiency.

Where GenAI projects get stuck

  • A convincing pilot that breaks on real data
  • No way to measure whether quality is improving
  • Costs and latency that do not survive scale
  • Security review blocking the launch
  • Uncertainty about what to build first
Discuss your generative AI project

What generative AI consulting covers

Generative AI consulting is the work of deciding what is worth building, designing how it should work, and making it dependable enough to run your business on. That spans use-case selection, model and retrieval choices, integration with the systems you already use, and the evaluation, security and cost work that decides whether it survives production.

What you get from an engagement

A clear read on feasibility and effort, an architecture you can defend internally, working software rather than slideware, task-level quality measurements, and documented handover so your team can operate and extend the system without us.

Engagements

From AI opportunity to production

  1. 01

    Discover

    Identify valuable use cases, assess feasibility and define the success metrics the system will be judged on.

  2. 02

    Build

    Design the architecture and validate the highest-risk assumptions with a working prototype instead of the easy demo.

  3. 03

    Productionize

    Build reliable agents and document workflows integrated with your existing systems and owned by your team afterwards.

  4. 04

    Evaluate and optimize

    Measure quality, secure the system and optimize latency and cost under realistic production workloads.

Application areas

Our strongest GenAI application areas

AI Agents & Automation

AI agents that execute real business workflows with controlled tools, scoped permissions, evaluation and human oversight, rather than open-ended chat.

  • Customer and internal support agents
  • Back-office process automation
  • Multi-agent workflows with tool calling
  • Agent evaluation and monitoring

Intelligent Document Processing

AI systems that extract, reason over and validate information across invoices, claims, contracts, forms and reports, with confidence thresholds and exception review.

  • Classification and information extraction
  • Contract and policy analysis
  • Multi-document reasoning
  • Straight-through processing with human review

Supporting engineering capabilities

  • LLM & SLM Engineering

    Fine-tuning, domain adaptation and retrieval so models perform on your data and terminology.

  • AI Performance & Cost Optimization

    Latency, throughput and cost per task engineered against real production traffic.

  • AI Evaluation, Observability & Security

    Task-level evaluation, tracing, guardrails and adversarial testing before and after launch.

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Questions

Common questions

What does a generative AI consulting engagement include?
Most engagements start with a focused assessment: the workflow, the data, the constraints and the use cases worth building. From there we either advise your team or build the system ourselves, through evaluation, security review and cost optimization.
Do you only advise, or do you build?
Both. We are engineers first, so advice is grounded in systems we have shipped. Some clients take the architecture and build it in-house; others have us deliver it end to end and hand it over.
We already have a prototype that is not production-ready. Can you help?
That is the most common starting point. We assess the existing system and identify what will fail under real traffic: accuracy, latency, cost, security. Then we fix those before scaling.
Can you work with our existing AI stack?
Yes. We work with your existing models, cloud infrastructure, vector databases, frameworks and APIs. Our role is to improve what you already have where that makes more sense than rebuilding from scratch.
How do you evaluate the quality of a generative AI system?
We create task-level evaluation datasets and measurable criteria, then use them for benchmarking, regression testing and ongoing production monitoring.
Can you help us choose the right LLM or AI model?
Yes. We evaluate models based on task quality, context requirements, latency, security, deployment constraints and cost rather than defaulting to a particular provider.