Mortiqo and AI-first CRM tools for mortgage advisers

A newer group of products is appearing in the UK mortgage and protection market, built on the assumption that a large part of a broker's admin can be drafted, checked or prompted automatically. Mortiqo is one example, positioning itself as an AI CRM for mortgage and protection advisers covering fact-finds, compliance checks, suitability reports, renewals and follow-ups.

The design difference is real. Established platforms mostly began as databases and added automation on top, so automation tends to sit at the edges. A product designed around generated output starts from the other end. Whether that produces a better daily experience for your firm is a question worth testing, but it should be tested alongside a different question: what it means to buy from a young supplier.

What the design difference looks like in practice

In an AI-first product, the expected behaviour is that you finish a conversation and a draft exists. The fact-find is structured without anyone typing it up, the case summary writes itself, the suitability document arrives as something to correct rather than something to compose, and the follow-up sequence proposes itself.

For a sole adviser or a small team with no administrator, that is an appealing shape. The work that usually happens in the evening is the work being targeted.

The corresponding risk is also structural. When a system produces polished text by default, the review step is the only thing standing between a draft and a client. In a firm under time pressure, review is the step that erodes. Any evaluation should focus at least as hard on how corrections are made and recorded as on how good the first draft is.

Buying from a newer vendor

This is the part that gets skipped, so make it explicit. None of these questions are hostile; a serious supplier will have answers.

• How long has the company been operating, and who is behind it?
• How many UK intermediary firms are using it in production, and can you speak to two of a similar size?
• What is on the roadmap versus available today, and will the contract distinguish between the two?
• What happens to your data if the business is acquired or ceases trading, and in what format can you extract everything?
• Where is data hosted and processed, and who are the sub-processors?
• What does support look like on a Friday afternoon in November?

Ask for the answers in writing. A smaller supplier can be more responsive and more willing to build what you need than a large one; the trade is concentration risk, and you price that by knowing how you would leave.

The questions that are specific to generated output

Ask how a suitability document is produced: which fields it draws on, what is generated, what an adviser must supply and whether the system will produce a document when information is missing. Ask what happens when the model is unsure — silence, a flag, or a confident guess.

Ask whether prompts and outputs are logged, whether the version an adviser approved is stored on the case record, and whether a reviewer can later see what was changed between the draft and the final document. That audit trail is what turns generated text from a liability into evidence.

Ask whether client data is used to train models, whether that is on by default, and whether it can be switched off. Get the answer from the contract, not the sales call.

How to pilot it without exposure

Start where a mistake is cheap. Internal case summaries, meeting notes, document checklists, draft follow-up emails and renewal reminders can all be tested without anything unreviewed reaching a client.

Run the pilot on live cases in parallel with your existing process for a defined period, so you can compare. Keep a simple log of corrections: what the system got wrong, how often, and how long the fix took. That log is the evidence you will need for a decision, and it is also what a compliance reviewer will want to see if you later adopt it more widely.

Only extend to client-facing output once the correction rate is known and an approval step is genuinely happening rather than nominally required.

Where these tools sit against established platforms

Larger, longer-established platforms tend to have deeper integrations into sourcing, lenders and verification services, more mature reporting, and more customers who have already found the edge cases. Newer AI-first tools tend to move faster on drafting, summarising and assistant-style workflow.

That is not a ranking. It is a description of two different bets. If your firm's cost is sitting in sourcing integration, network reporting or oversight across a dozen advisers, an AI-first CRM may not reach far enough. If your cost is repetitive fact-find and document admin in a small team, it may be exactly the right size of tool.

Some firms will end up running both: an established system of record and a lighter assistant layer. That works only if you have decided which one holds the truth.

Bottom line

AI-first CRM tools are worth watching because they aim directly at the admin that makes broker work long. Evaluate them on two axes rather than one: what the product does, and what the supplier is. Pilot narrowly, keep advisers in the approval seat, log the corrections, and confirm capability, data handling and pricing with the vendor rather than relying on any summary of the market, including this one.