Practice 04 — AI & LLM engineering
Custom AI that runs on your terms.
We customize, deploy, and integrate large language models for organizations that need more than a chatbot: models adapted to your domain, running where your data is allowed to live, and connected to the systems your people already use.
Capabilities
From model selection to production.
Most AI projects stall somewhere between an impressive demo and a system people can rely on. We cover the whole distance: choosing and adapting the model, grounding it in your data, running it securely, and proving that it works.
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01
Model selection & evaluation
We test commercial and open-weight models head to head on your real tasks, not leaderboard averages, and choose the smallest model that clears your bar.
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02
Customization & fine-tuning
Adapt a model to your domain, terminology, and output formats with fine-tuning and preference tuning, built on carefully curated training data.
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03
Retrieval over your knowledge
Ground answers in your documents, policies, and records, with retrieval that cites its sources and respects who is allowed to see what.
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04
Private deployment
Run open-weight models on your own GPU servers or private cloud, tuned for speed and cost with quantization and efficient serving.
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05
Assistants & agents
Assistants that can act as well as answer, connected to your systems through well-defined tools and the Model Context Protocol (MCP), with people in the loop.
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06
LLM gateways
One governed front door to every model: single sign-on for people, scoped keys for applications, usage and cost tracking, and complete logs.
The build-out
Every layer, from the surface to the sea floor.
A useful AI system is much more than a model. We design and build each layer, or fit into the ones you already have.
Surface: what people touch
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01
Experience
Where people meet the system.
- Chat & copilots
- Workflow automation
- APIs for your applications
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02
Gateway
One governed front door.
- Single sign-on
- Access policies
- Usage & cost tracking
- Audit logs
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03
Orchestration
Where the model meets your business.
- Agents & MCP tools
- Retrieval
- Guardrails
- Human review
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04
Models
The right model for each job.
- Commercial APIs
- Open-weight
- Fine-tuned
- Distilled
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05
Infrastructure
Where it all runs.
- Your GPU servers
- Private cloud
- Managed endpoints
Sea floor: where it runs
Across every layer
Evaluation & monitoring
Test suites drawn from your real work, run before every change. In production, we track quality, latency, and cost continuously.
Customization
As custom as it needs to be, and no more.
Each step down the ladder costs more to build and to maintain. We start with the lightest technique that meets your quality bar, and go further only when the evaluation results say it’s worth it.
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Step 1
Prompt & context design
Instructions, worked examples, and structured outputs. Fast to build and easy to change.
Best forWell-defined tasks a capable model already understands.
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Step 2
Retrieval (RAG)
Answers grounded in your documents and kept current without retraining the model.
Best forKnowledge that changes often, and answers that need citations.
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Step 3
Fine-tuning
The model learns your domain’s language, style, and formats from your own examples.
Best forSpecialized terminology, strict formats, or a smaller and faster model.
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Step 4
Custom models
Distilled or further-trained models that you own outright and can run anywhere.
Best forHigh volume, tight latency or cost targets, or unusual domains.
Trust & governance
Built for data that can’t leak.
The organizations with the most to gain from AI often hold the most sensitive data. We design for that from the first conversation.
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Your data stays yours
Private deployments keep prompts, documents, and outputs inside your environment, and we configure every service so your data never trains a third-party model.
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Access that follows your roles
Single sign-on for people, scoped keys for applications, and retrieval that only returns what each user is allowed to see.
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A complete audit trail
Every request and tool call logged, so you can see who asked what, which sources were used, and what the model did.
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Evaluation you can show
Test suites built from your real work, with results you can hand to a review board before anything reaches production.
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People stay in charge
Agents propose and people approve. High-stakes actions wait behind a human checkpoint, and every decision is recorded.
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Governance built in
Documentation and risk controls mapped to the framework you follow, such as the NIST AI Risk Management Framework or ISO/IEC 42001.
Questions
What teams ask us about AI.
Can we use AI without sending our data to a third party?
Yes. Open-weight models now handle a wide range of business tasks well, and they can run entirely on your own GPU servers or in a private cloud. Where a commercial model is the better fit, we set it up under terms that keep your data out of training, and route it through a gateway you control.
Should we fine-tune a model or use retrieval?
Usually retrieval first. It keeps knowledge current and answers traceable to their sources. Fine-tuning earns its place when you need a specific style, strict output formats, specialized terminology, or a smaller model that runs faster and cheaper. We decide with evaluation results, not intuition.
How do you know whether it actually works?
Before we build, we agree with you on an evaluation set drawn from your real work, and on what a good answer looks like. Every change is measured against it, and in production we keep tracking quality, latency, and cost.
What does it cost to run?
It depends on volume, model size, and latency needs. Before you commit, we model the cost of commercial APIs against self-hosting for your expected usage, so you can choose with real numbers in front of you.
Have a system that needs building, or rebuilding?
Tell us what you’re working on. We’ll help you find the clearest way forward.