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Service · Backend, cloud & AI
Practical AI features and workflow automation — document processing, assistants over your own data, and integrations that remove repetitive work.
Overview
AI is useful when it is pointed at a specific, well-understood task: reading documents, drafting responses, classifying requests or answering questions from your own knowledge base. We start by identifying those tasks and how success will be judged, before choosing any model.
Not every problem needs AI. Many repetitive processes are better solved with conventional automation — integrations, rules and scheduled jobs — which is cheaper and fully predictable. We recommend whichever fits, and often combine the two with a person reviewing important decisions.
Problems we solve
The problem
Staff spend hours copying information from emails, PDFs and forms into other systems.
Our approach
Automated extraction and validation, with uncertain cases routed to a person for review.
The problem
Answers to common questions are buried in documents nobody can find.
Our approach
An assistant that searches your own content and cites its sources, restricted to the data each user may see.
The problem
Routine tasks are triggered manually between tools.
Our approach
Event-driven workflows that move data, send notifications and create records automatically.
Features
Identifying where AI adds value, and where simpler automation is better.
Extracting structured data from invoices, forms and correspondence.
Retrieval-augmented answers grounded in your documents, with citations.
Integrations, rules and scheduled jobs across your existing tools.
Confidence thresholds and approval steps for important outputs.
Test sets, quality checks and usage and cost tracking in production.
Deliverables
Technologies
Final technology choices are agreed with you during discovery.
Development process
Every step ends with something you can review, so priorities can be adjusted before the next one starts.
Pick a specific task, gather real examples and define what good output looks like.
A small proof of concept measured against your examples.
Review accuracy, cost and risk together before investing further.
Production build with review steps, logging and access controls.
Ongoing evaluation, prompt and model updates, and cost tracking.
Use cases
Turning documents into structured records.
Classifying and routing requests, and drafting replies for staff to approve.
Answering staff questions from policies, manuals and past work.
Syncing data between tools and removing manual hand-offs.
No. Language models can be wrong or invent details. We design for this with grounding in your own data, source citations, confidence checks and human review where mistakes matter, and we measure accuracy against real examples before launch.
We document exactly which data is sent to which service and choose providers and settings that match your requirements — including models hosted in your own environment when data must not leave it.
Often just automation. If a task follows clear rules, conventional integration is cheaper and more predictable. We recommend AI only where it adds value that rules cannot.
We choose appropriately sized models, cache where possible, limit usage per user and monitor spend, so costs are visible and predictable.
Share a few details about your goals. We will reply with questions, a suggested approach and the next steps.