AI automation means putting a model inside a workflow your business already runs — scoring a lead, reading a document, drafting a reply — so a step that used to take a person twenty minutes takes nobody any minutes. It is integration work first and AI work second, and it fails when people get that order backwards.
Most teams do not need a chatbot. They need the twelve manual handoffs between their forms, their CRM, and their spreadsheet to stop being manual.
We map how work actually moves through your business, then pick the one or two steps where automation pays for itself fastest. Usually it is intake, triage, or anything that ends in someone copying data between two screens.
Your forms, CRM, database, billing, and inbox get connected with n8n or direct API integrations. This is the unglamorous part that determines whether the AI layer is useful or theatre.
Classification, extraction, scoring, drafting, summarising. The model handles judgement-shaped work inside a workflow that has guardrails, a fallback, and a human checkpoint where the stakes justify one.
An internal dashboard or a channel that shows what ran, what it decided, and what needs a human. Automation nobody can observe is automation nobody trusts.
Lynx.ai for Tiger Finance runs exactly this shape: Webflow captures the lead, n8n orchestrates, an AI valuation step scores the property, Supabase holds the record, and agents work it from a FlutterFlow dashboard.
Tiger Finance
An AI-powered real estate lead generation system with an agent dashboard for Tiger Finance.
2024
Lynx.ai is a proof of concept that brings AI into the real estate lead generation process. Webflow captures leads, n8n orchestrates the automation, Supabase stores the data, and agents manage everything from a custom FlutterFlow dashboard.
Every project is scoped and priced individually. Your quote will be fixed in writing before anything starts.
It depends on how many systems the workflow touches and whether staff need an interface for it, so every build is quoted individually. Tell us what the manual step is today and you will get a written scope and a fixed price within two business days. Ongoing tuning after launch is normally a retainer rather than a project.
A focused workflow is usually running in production within two to three weeks. Anything touching more than three systems, or needing a custom interface for staff, runs closer to four to six weeks. The integration work takes longer than the AI work almost every time.
No, and you usually should not. The work sits on top of what you already run — HubSpot, Sheets, Xero, Slack, your existing site. Ripping out working systems to make room for an automation project is how these builds go over budget.
Claude and OpenAI models for language work, n8n for orchestration, Supabase or Firebase for data, and direct API integrations where a platform-neutral connection matters. Tool choice follows the problem; nothing here is locked to one vendor.
Every workflow ships with a confidence threshold, a fallback path, and a human review step wherever the cost of being wrong is real. You will also get a log of what the system decided and why, so a bad call is findable rather than mysterious.
2026
A repeatable AI video pipeline for a photoreal CGI mascot, built so the character never drifts between clips.
2026
A free, prize-backed survivor football platform rebuilt so the game engine never knows which competition it is running.
2024
A headless CMS brand style guide platform for managing and sharing brand assets across social and advertising.
Thirty minutes on a call is usually enough to work out whether this is a two-week job, a two-month job, or something you should not build at all. You get a written scope and a fixed price before anything starts.
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