LLMs wired into
systems that already run
Language models wired into systems that already run — retrieval over your documents, queues a person still clears, inference you can host. Send the brief. Estimate within 24 hours.
AI engineering capabilities
Model choice is the smallest decision on the list. The work is in retrieval quality, output validation and what happens when a call fails.
LLM integration into existing systems
Model calls wired into the software you already run — CRM, ERP, ticketing, internal portals — behind a service boundary, with retries, cost accounting and a fallback path when the provider degrades.
RAG pipelines over your own data
Ingestion, chunking, embedding and retrieval tuned to your document structure. Answers cite their source, and the index is rebuildable from the original files at any time.
Document and text automation
Classification, extraction, summarisation and normalisation of documents that currently move through a human queue. Structured output validated against a schema before it reaches your database.
Workflow automation
Multi-step processes where a model handles the judgement and deterministic code handles everything else. Each step is observable, replayable and reversible.
Prompt engineering and fine-tuning
Prompt versioning, evaluation sets and regression checks before a change ships. Fine-tuning only where prompting and retrieval genuinely run out of headroom.
Self-hosted inference
Open-weight models such as Llama and Mistral served on your own hardware or private cluster, for workloads where data cannot leave the perimeter.
Models, retrieval & serving
Providers are interchangeable by design — the integration layer is ours, so a model swap is a configuration change rather than a rebuild.
What runs under the hood
- Node.js
- TypeScript
- React 19
- REST / streaming APIs
- PostgreSQL
- Vector indexes
- ClickHouse
- Object storage
- Docker
- Kubernetes
- Worker pools
- RabbitMQ / Kafka
- OpenTelemetry
- Sentry
- Structured logging
- Cost dashboards
Connected expertise
Cloud & on-prem→
Where inference and indexes run, and what it costs to keep them running.
CRM, ERP & CMS→
The business systems most of this automation reads from and writes back into — see the live CRM operations desk.
Dashboards→
Review queues, evaluation results and usage cost surfaced for the team that owns the workflow.
Legacy modernization with AI assistance
Most operator systems are not greenfield. We use assistants to accelerate inventory, characterisation tests and mechanical refactors — while keeping cut-over, protocol semantics and data integrity under human ownership.
The payoff for the client is speed without a second, invented architecture. Context packs and rule sets keep every agent session aligned with the stack already in production.
- Characterisation tests before behavioural change
- Seam-first extraction instead of big-bang rewrites
- Protocol and telemetry semantics reviewed by domain engineers
- Shared AI rules so assistants respect the living codebase
AI inside existing products
Send the brief. Estimate in 24 hours.
The workflow, the data it touches and where it stalls. We return a scoped plan and an honest cut line within one business day.