AI that does actual work.
Assistants, document processing, semantic search and workflow automation wired into your systems — with guardrails, evaluation and a cost ceiling.
Overview
A chatbot is not a strategy.
Most AI value in a normal business is unglamorous: reading documents, classifying enquiries, drafting replies, summarising calls, answering questions from internal knowledge. Those tasks have measurable time savings and low tolerance for nonsense.
We build AI features into working systems — connected to your data, with retrieval over your own documents, structured outputs your code can trust, and a human in the loop wherever the cost of being wrong is high.
Every build ships with an evaluation set and cost monitoring, so quality and spend are both visible instead of a monthly surprise.
What we provide
Everything the build actually needs.
Use-case assessment
Which tasks are actually worth automating, what the saving is, and what the risk is if the model is wrong.
AI assistants
Support and internal assistants grounded in your documentation, with escalation to a human.
Document processing
Extract structured data from invoices, forms and PDFs into your systems.
Semantic search & RAG
Search that understands intent, built over your own content with source citations.
Workflow automation
Classification, routing, drafting and summarisation embedded in existing processes.
Evaluation & guardrails
Test sets, output validation, fallbacks, rate limits and cost dashboards.
Key features
What you get shipped.
- Retrieval over your own documents with citations
- Structured, schema-validated model outputs
- Human-in-the-loop review on high-risk actions
- Evaluation suite run against every prompt change
- Per-feature cost tracking and spend limits
- PII handling and data-retention controls
- Model-agnostic architecture to avoid vendor lock-in
- Fallback behaviour when the model is unavailable
Technology stack
Tools we actually use.
Process
How the work runs.
Assessment
Shortlist tasks by time saved, data availability and risk.
Data readiness
Gather, clean and structure the content the model will rely on.
Prototype
A narrow version tested against a real evaluation set.
Integrate
Wire into your systems with validation, guardrails and logging.
Monitor & tune
Track accuracy, cost and usage; refine prompts and retrieval.
Industries served
Sectors we know the constraints of.
Relevant work
Projects in this space.
Why EHUB
Why teams keep us on the project.
Task-first, not tool-first
We start from the work being done, not from whichever model launched last week.
Evaluated, not vibes-tested
Every feature has a test set, so changes are measured instead of guessed.
Cost visibility
Token spend is tracked per feature with hard limits configured.
Integrated properly
AI lives inside your workflow, not in a separate tab nobody opens.
FAQs
Frequently asked.
Will our data be used to train public models?
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Not when configured correctly. We use API tiers with no-training terms and set data retention policies explicitly.
How do you stop the model making things up?
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Ground answers in retrieved source content, validate outputs against a schema, cite sources, and route low-confidence cases to a human.
What does it cost to run?
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Usage-based. We estimate per-transaction cost during the prototype and set hard spending limits before launch.
Can you add AI features to our existing product?
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Yes — that is most of our AI work. We build into existing applications rather than replacing them.
Which task should the machine take?
Tell us the repetitive work your team does daily and we will assess whether AI genuinely helps.
PARTNERS, PLATFORMS & PROFESSIONAL NETWORKS
