Services / AI Systems
Practical AI engineering embedded in real workflows.
We design and deploy AI systems for defined business tasks: retrieval and semantic search, custom models and embeddings, and supervised agents integrated with the tools your teams already use.
Production Systems
AI applied where the task is clear.
A useful AI system begins with a defined task and an operating context. We work on applications such as extracting structured information from documents, searching internal knowledge, or preparing repetitive updates inside an existing workflow.
-
01
Retrieval-Augmented Generation (RAG) & Semantic Search
We build retrieval pipelines over selected company knowledge, such as contracts, technical material, support records, or procedures. Depending on the use case, responses can include source references and checks that help people assess whether the retrieved material supports an answer.
-
02
Supervised Operational AI Agents
Agents can classify incoming requests, extract fields, check them against business rules, and prepare actions in an ERP or CRM. The workflow can route uncertain or consequential cases to a person before anything is committed.
-
03
API Glue & Workflow Integration
We connect model services to existing tools and data through scoped integrations, with access controls, logging, and safeguards appropriate to the deployment.
-
04
Fine-Tuning & Domain Adaptation
When the use case and available data justify it, we can prepare domain-specific datasets and evaluate fine-tuning or other adaptation approaches against the baseline model.
Our Guardrails
Designed for review, control, and clear limits.
AI outputs are probabilistic, so production systems need to account for uncertainty. We design around the cost of errors, the sensitivity of the data, and the points where a person should review or approve an action.
-
Rule 1
Strict Human-in-the-Loop Safeguards
For consequential workflows, the system can prepare a recommendation or draft while a person reviews and approves it before execution. The right approval boundary depends on the task and its risk.
-
Rule 2
Deployment choices matched to data sensitivity
We assess data handling, provider terms, access controls, and deployment options as part of the design. Where requirements call for it, this can include enterprise endpoints or self-hosted models; the specific data flow is agreed before implementation.
-
Rule 3
Verifiable Audit Trails
Where useful and technically practical, we retain records of inputs, retrieved sources, model outputs, and workflow decisions so teams can inspect and improve system behaviour.
Next Step
Evaluate practical AI for your operations.
We can help assess whether a use case calls for a model, a retrieval system, conventional software, or a mix of approaches, and what would need to be true for a production deployment to be worthwhile.