AI-powered business tools

What it is

A focused application — a quoting desk, a document reader, a proposal composer, a ranking view — built around one AI capability and the data it needs. The model does the reading, extraction or drafting; the tool gives the team a surface to check it, correct it and send it on, while the source records stay inside the client's own systems.

Why businesses need it

Most businesses run on tasks that are mostly reading: a quote that needs the right product and rate located, a tender clause that needs finding, an inbox that needs sorting into the correct queue. Spreadsheets and email were not built for it, and a bare chat window has never seen your price list. The tool puts the capability where the work already happens, with the checks the work already needs.

What MIWEBX delivers

• Quoting and estimating desks that read a requirement and assemble a priced answer from the catalogue
• Document extraction for invoices, contracts, tenders and certificates into structured fields
• Proposal and tender responses drafted from approved content, past work and live client data
• Support and inbox triage that classifies, summarises and suggests the next owner
• Summarisation and search across a company's own corpus, with the source attached
• Scoring and ranking of leads, applicants, tenders or tickets against criteria the team sets

Core features

• Source records read in place — the database, drive or CRM the client already owns
• Every generated field carries the passage, file or row it came from
• Confirm before write: nothing reaches a customer or another system without a person approving it
• Criteria, thresholds and templates configured by the team and versioned, not buried in a prompt
• Role-based access, so the tool sees only what that user could already see
• Bulk mode for the recurring case and single-item mode for the awkward one
• Empty, uncertain and failure states designed in, because they happen constantly

Technical capabilities

• Provider abstraction layer: OpenAI APIs, Anthropic APIs or an open-source model chosen per task, language and budget
• Retrieval over the client's own data, with permission filters applied before ranking
• Schema-constrained output that is validated or rejected rather than silently mistranslated
• Evaluation set of real examples per tool, replayed whenever a prompt, model or index changes
• Guardrails for refusal, out-of-scope requests and personally identifying data
• Caching, batching and a token budget per run so a routine job does not pay full price daily
• Structured logging of inputs, decision and cost, with secrets and personal data redacted

How we build it

• Sit with the people doing the task and write down each step, including the exceptions
• Decide which data the tool reads and where it is ever allowed to write
• Build one screen against real records, with the model behind it
• Grade the output against work the team has already accepted
• Add the approval step, permissions and audit trail before rollout
• Put it in front of one team, then extend once the failure cases are known

Technology stack

• OpenAI APIs
• Anthropic APIs
• Open-source LLMs
• Embeddings
• Vector databases
• Next.js
• Python
• PostgreSQL
• S3-compatible storage

What you receive

• The tool, deployed in the client's own account and infrastructure
• Documented data connections and a permission model per source system
• Editable configuration for criteria, rules and templates
• The evaluation examples and scorecard the tool was judged against
• Review queue, audit log and an admin view of usage and cost
• Handover documentation and a recorded walkthrough for the team

Business benefits

• A task stops depending on the one person who knows how it is done
• Newer staff produce a first draft in the shape a senior one would
• Reading and re-typing time comes back to the actual judgement
• Quoting and response turnaround stops being the bottleneck in the funnel
• Data stays inside the systems it always lived in
• Rules sit in configuration the business can change itself