AI & Automation
Automate the repeated work. Use AI only where it earns its place.
Most of what gets sold as AI is a scheduled job with a language model bolted on. Some of it should be. The useful question is which repeated task is costing you real hours, and whether the cheapest reliable fix is a rule, an integration, or a model — in that order of preference, because the first two do not hallucinate.
Problems we see in this category.
If two or three of these describe your operation, this is the right page.
- Staff retype the same information between systems, or from an email into a system.
- Enquiries are qualified by hand, slowly, and the slow ones go cold.
- Someone spends hours a week producing a document that is assembled from records you already hold.
- Answers to routine internal questions require a person who knows where to look.
- You have been quoted for an AI project and cannot tell whether the problem needs one.
Deliverables.
- A shortlist of candidate tasks with an estimate of the hours each consumes now, gathered from the people doing them rather than assumed.
- A build-or-skip recommendation per task, including the ones where the honest answer is that automation is not worth it.
- The automation itself, built against your real data and your real edge cases.
- Where a model is genuinely the right tool: an assistant grounded in your own records that returns the record behind each answer, so a claim can be checked.
- A monitoring and failure plan, because an automation nobody watches is a liability rather than an asset.
Process.
We start from the task, not from the technology. A rule beats a model when a rule works, an integration beats both when the data already exists somewhere, and a model is worth its cost when the input is genuinely unstructured. Where a model is used, it is grounded in your records and it cites them.
Work in this category.
Each of these has a destination you can open and a write-up that states what was delivered, what it connects to, and what has not been verified.
Tophat
An enterprise business suite for a commercial real estate brokerage, with an assistant that answers from the brokerage's own rows and cites the record it read.
Read it ›ServiceLens / DiagBuddyGO
A field-service platform where dispatch, diagnosis, parts and inventory are one workflow, and the diagnostic answers are grounded in the technician's own history.
Read it ›Fix Appliances Now
A lead-capture and routing platform for an appliance repair operation, with a symptom checker on the front and partner scoring behind it.
Read it ›Bring one task you think should be automated. We will tell you plainly whether it should be.
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SOPHIA XT is the engineering arm of 3 Point Forward.
3 Point Forward
3 Point Forward does the business work: diagnosing what is actually wrong with an operation, planning the implementation, applied-AI consulting, training the staff who will run it, and managing the engagement.
SOPHIA XT — the engineering arm
SOPHIA XT does the engineering: custom business software, integrations, AI engineering, developer tooling and the research underneath it.
One company, two jobs. 3 Point Forward defines the operational problem and the implementation plan; SOPHIA XT builds what the plan needs. A company that already knows what it wants built can go straight to SOPHIA XT, and a company that needs the problem defined first should start at 3 Point Forward. Either door reaches the same team.