Free checklist Book a call →
Home/ Industries/ Financial services software and AI

Industries

Software and AI for banks, lenders and fintechs

We build AI agents, internal tools and customer-facing sites for financial services firms, then run them. Every agent ships with guardrails, a full audit trail and a rollback path, because here a wrong answer becomes a complaint file.

Book a call → Get the CTO checklist

Where we help

Software and AI for banks, lenders and fintechs, end to end.

Support copilots for regulated teams

An assistant that drafts replies for your support or operations staff from your own policy documents and account data. It suggests. A person sends. Every draft is logged with the sources it drew on.

Guardrails and refusal design

We decide with you what the agent must never do: no advice, no balances without verification, no promises about outcomes. Those limits are enforced in code and tested, not written into a prompt and hoped for.

Audit trail and evidence capture

Every request, retrieved document, model response and human edit is recorded with a timestamp. When compliance or a regulator asks what the system told a customer in March, you can answer with the record.

Internal operations tooling

Queue tools, case workflows, reconciliation views and approval screens for teams currently working out of spreadsheets and shared inboxes. Role-based access, so people see only what their job requires.

Websites and product marketing sites

Webflow and headless CMS builds for lenders, brokers and fintech products. Structured so marketing can publish rate tables, disclosures and product pages without waiting on a developer.

Document and data pipelines

Statements, KYC packs, application forms and provider exports parsed into structured data your systems can use. Failures surface for review rather than passing bad records through quietly.

How it runs

Plan. Build. Run.

01

A 30 minute call

You describe the process, the volumes and who is accountable for it. We say plainly whether this is something to automate, something to instrument first, or something that should stay manual.

02

Fixed scope, timeline and price

We write down what gets built, what it will not do, which systems it touches and what it costs. One number, one date. You approve it before any code is written.

03

Build with a human in the loop

We build in short cycles you can see. Agents start in draft-only mode with a person approving every output, so you can read the logs and judge the quality before anything is customer-facing.

04

Launch, then run

We go live behind a switch that can be turned off in seconds. After launch we monitor, fix, patch and improve on a retainer, with a monthly report of what changed and what it did.

Why infoloop

We do not hand over and leave.

  • We run what we buildMost agencies hand over a repository and a login. We stay on: monitoring, fixes with response targets, security updates and a monthly report of what changed.
  • Rollback is designed in, not improvisedEvery agent has an off switch and a previous known-good version. If output quality drifts or a policy changes, you revert to a safe state without waiting for a release cycle.
  • We say no to bad automationIf a process is too ambiguous, too poorly documented or too consequential to automate safely, we tell you before you spend money on it. Fewer projects, fewer regrets.
  • Production, not pilotsWe have put 50+ products into live use across six countries at 99.9% uptime. Our interest is in systems that survive contact with real customers, not demos.
  • One team from scope to supportThe people who scoped it build it and answer the phone afterwards. No handover to an account manager who was not in the room.

What you get

Every engagement includes these, in writing, before work starts.

  • A written scope with an explicit list of what the system will not do or decide
  • Guardrail rules enforced in code, with tests covering each refusal case
  • A complete audit log of prompts, sources, responses and human edits, exportable
  • A rollback switch and a documented previous version to revert to at any time
  • Monitoring with alerts, plus a defined response target for incidents and fixes
  • A monthly report covering uptime, changes made, issues found and what we suggest next

Who this is for

Three situations where this is the right call.

A support queue growing faster than headcount

Volumes are up, answers live in policy PDFs and long-serving people's heads, and hiring is slow. A copilot that drafts for your agents cuts handling time without letting a model speak to customers unsupervised.

A pilot that compliance will not sign off

Someone built a promising agent, but there is no audit trail, no defined refusal behaviour and no way to roll back. We rebuild it so the control questions have real answers, or tell you it should not ship.

Operations running on spreadsheets and inboxes

Cases tracked in a shared file, approvals given over email, nobody sure who changed what. We build the tool that should have been there, with roles, history and a record of every decision.

Proof

Software we built, and still run.

Fintech support copilot

Read it →

Responsible AI

Read it →

Security

Read it →

Questions

What buyers ask us first.

How do you stop an AI agent giving financial advice or a wrong answer?
Three ways, none of them prompt wording alone. First, scope: the agent answers only from your approved documents and data, not from general knowledge. Second, refusal rules enforced in code and covered by tests, for the things it must never do, such as advising on products, quoting balances without verification, or predicting outcomes. Third, a human in the loop for anything customer-facing: the agent drafts, your team reads and sends. Most of our financial services work stays in draft-only mode permanently, because the value is in drafting speed, not in removing the person.
What does the audit trail actually record?
Every request that reaches the system, the documents or records it retrieved, the response the model produced, any edit a human made before sending, the final output, and timestamps and user identity throughout. It is stored in your infrastructure and exportable. The point is that when compliance, an auditor or a regulator asks what the system told a specific customer on a specific date, you can produce the record rather than reconstruct it. We agree retention periods with you at scoping, since your obligations differ by market and product.
What does an engagement cost and how is it structured?
We do not publish rates: a support copilot over a documented policy set and a rebuilt operations platform are different orders of work. The shape is always the same: a 30 minute call, then a written scope with a fixed timeline and a fixed price before anything is built. One number, one date, and a clear list of what is excluded. Running the system afterwards is a separate monthly retainer covering monitoring, fixes with response targets, security updates, improvements and a monthly report. You can take the build without the retainer, though we will tell you honestly why we advise against it.
What happens after launch?
We run it. That means monitoring the system and alerting on failures, fixing defects within agreed response targets, applying security updates to dependencies and infrastructure, making agreed improvements, and sending a monthly report covering uptime, what changed, what broke and what we recommend next. For AI systems we also review output quality against the logs, because model behaviour and your own policies both drift over time. If quality degrades or a policy changes mid-month, we can roll back to the previous known-good version immediately rather than waiting for a release.
Do you have experience in financial services specifically?
Yes. We built a support copilot for a fintech, which is the closest match to most of the enquiries we get from this sector. Across all industries we have shipped 50 or more products in six countries at 99.9% uptime. We are a software and AI firm rather than a regulatory consultancy: we build systems that produce the evidence your compliance function needs, and we work to the control requirements your team defines. We do not advise on what those requirements should be, and we hold no financial services certification or accreditation.
Where does our data go, and can this run in our own environment?
Yes, and for most financial services clients that is the default. We deploy into your cloud accounts, against your data stores, under your access controls. Customer data need not leave your environment except for the model call itself, and we can redact identifiers before the request and retrieve only the minimum context needed to answer. Model provider, region and retention settings are decided with you at scoping rather than assumed. If your policy rules out third-party model providers entirely, say so on the first call and we will tell you honestly what is still possible.

Related

You might also need.

AI & Advanced Tech Solutions

Agents and copilots that do real work in production, with guardrails and rollback.

See more →

Enterprise Solutions

ERP, system integration, workflow automation and the APIs that hold them together.

See more →

Application Maintenance & Support

Monitoring, fixes with response targets, and a monthly report you receive.

See more →

Tell us the process you want to make safer

A 30 minute call, then a written scope with a fixed price and date. If the process is too ambiguous or too consequential to automate safely, we will tell you that instead of quoting for it.

Book a call → Get the CTO checklist
Book a call Checklist