Knowledge your team can use
RAG and knowledge retrieval across your documents, with source citations and permissions that reflect who should see what.
Services / Build
Applied AI and agents,
built around your work.
Enterprise knowledge assistants, document workflows and AI-assisted recruitment research. We build around your sources, systems and decisions, with evaluation and human review.
Illustrative workflow
Every answer keeps its receipts.
The assistant reads only the documents the person may see, compares contract versions, cites each statement and shows what it could not find, so the answer can be checked.
Beyond the first demo
The model is one part.
Your business is the rest.
A promising answer in a chat window is a beginning. A business system also needs the right source material, access controls, connections to other tools, and a way to know when the answer is wrong.
Start with a task worth improving. Then build and evaluate the system with the people who will use it.
What we build
From the right question to a working system.
Agents that move work forward
Connect planning, research and actions across a multi-step workflow. Define tools, approval points and boundaries for each agent.
Models fitted to the task
Evaluate hosted and open-weight models. Fine-tune models such as Llama or Mistral when your dataset and task justify it.
Governance built into delivery
Address data access, PII redaction, prompt injection and audit logging alongside the application, with controls matched to your risk.
A practical AI roadmap
Assess data readiness, select useful first projects and establish how you will evaluate their value before investing in a larger build.
Document workflows in existing systems
Extract and check information, preserve source references and route exceptions for review. Connect the results to the applications your business already uses.
AI-assisted recruitment research
Turn a role brief into an editable research plan and candidate longlists with supporting excerpts, leaving assessment and selection with your recruiters.
A useful place to start
From a document
to a decision.
In a document-heavy reporting workflow, the system should return information a specialist can check, correct and act on. The same approach fits knowledge search, quotation preparation and internal assistants.
See how source records carry through document intelligence for emissions reporting and Bond Services’ connected document and billing workflows.
- 01 / CONTEXT
Bring the source with the answer.
Retrieve relevant documents and preserve the references needed to check an output.
- 02 / EVALUATION
Test against the actual task.
Use representative inputs and domain feedback to find the failures that matter.
- 03 / ACTION
Keep people in control.
Route uncertain or consequential decisions for review before a system takes action.
AI-assisted recruitment
Research a role.
Keep the evidence attached.
A recruitment research workflow can connect notes, resumes and earlier searches to a new brief. An editable plan makes the criteria explicit; source-linked assessments give recruiters material they can inspect before making a decision.
Explore evidence-backed candidate longlistsEngineering foundations
Choose the stack around the problem.
We choose models and tools on quality, privacy, latency and running cost. These are ones we work with.
Models
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- Cohere
Orchestration
- LangChain
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- Vercel AI SDK
Retrieval & data
- Pinecone
- Weaviate
- Qdrant
- ChromaDB
- Supabase
Deployment
- AWS Bedrock
- Azure OpenAI
- Google Vertex AI
- Hugging Face
- Python / FastAPI
- Private cloud & on-premise
How we deliver
Build confidence at every step.
Discovery
Understand the workflow, data readiness and risks. Agree what a useful result looks like.
Proof of concept
Test feasibility on a bounded use case. Use realistic inputs and clear evaluation criteria.
Production operations
Integrate the system and add monitoring, caching, access controls and recovery paths.
Optimization
Use user feedback, evaluation results and cost data to guide the next improvement.
Questions
Common questions.
What applied AI workflows can you build?
We build enterprise knowledge assistants, document processing and review workflows, and AI-assisted recruitment research. Delivery can include source-connected retrieval, application integrations, evaluations and human approval steps, scoped around the task your team needs to perform.
How does AI-assisted recruitment research support our recruiters?
It connects a role brief to existing candidate records, an editable research plan and longlists with supporting excerpts. Recruiters can review the criteria, inspect source evidence and record their own decisions separately from the generated assessments.
Is my data safe? Do you train on it?
We design around your confidentiality and access requirements, and do not use your proprietary data to train public models. We agree data handling, model-provider terms and NDA requirements before implementation.
Do you use open source models?
Yes. Open-weight models such as Llama and Mistral can run within a private VPC. We compare them with hosted models against your quality, cost and data-privacy requirements.
How do you handle hallucinations?
We use Retrieval-Augmented Generation (RAG) to ground outputs in your documents, alongside evaluations such as RAGAS, source citations and guardrails. We test with representative inputs and add human review where errors carry significant consequences. These controls reduce risk; they do not make AI infallible.
Can we deploy this on-premise?
Yes. We can containerize suitable AI components with Docker and Kubernetes for your servers or private cloud. We assess model licensing, hardware and external dependencies before committing to an air-gapped deployment.
How do we scope an applied AI project?
We identify the workflow, representative inputs, data access and integration needs, then agree evaluation criteria and a bounded first build. The delivery plan covers human review, operational responsibilities and the evidence needed before a wider rollout.
What is the cost structure?
We typically operate on a project-based fee for the initial build, followed by a retainer for ongoing maintenance and optimization. For larger strategy engagements, we can discuss a time-and-materials model.
Bring us a task worth improving.
Tell us where work gets stuck. We’ll explore what AI can usefully change and what it will take to build it.