Applied AI for financial services
Applied AI, from decision to delivery.
For financial firms where being wrong is expensive and slow is costly. Agents do the research and the building. A named expert owns the conclusion and the production change.
Discussion draft · October 2026 · hello@advancedapplied.ai
- Approved data only
- If a source is not approved, the job cannot reach it.
- Identity on every request
- Every job resolves to the person who started it. Agents inherit that identity.
- Audit trail to sign-off
- Which model, which sources, what was written, and who reviewed it.
- A named owner
- One person signs each conclusion. Accountability is not delegated to a model.
- Containerized sandbox
- Delivered in a sandbox your risk function can inspect.
Every output carries its evidence trail. The control is shown as an artefact, not a claim.
The problem
AI pilots stall between a good demo and a governed production system.
So the productivity gains are slow to arrive.
No accountable owner
A model produces an answer. Nobody signs the conclusion.
Not in the workflow
The prototype sits beside the process, not inside it.
Governance arrives late
Data, identity and audit questions surface after the demo, not before.
The offer
Decide. Automate. Deliver.
Three kinds of work, one team. We start with one decision or workflow, take it into production under your governance, and hand your team the method for the next one.
See how an engagement runs →Decide
Agentic research with verified conclusions and a named owner. From rates and commodities factor models to deal sourcing and screening.
Rates factor model · Commodity factor model · Deal sourcing and screening
Automate
AI inside the workflow: confirmation matching, KYC screening, and sourcing and ranking of candidates.
ISDA confirmation matching · KYC screening · Candidate and PM ranking
Deliver
Prototype to production under FS governance, on containerized infrastructure, with data, app development, DevOps and testing.
Front-office analytical projects · Managed AI sandbox · Data and DevOps
Transfer
Operating model, roles and handover, so your team runs the next one.
Method · Roles · Runbooks
How we work
Start with one decision or workflow.
Four stages. Each one has an exit criterion and an artefact your team and your risk function can inspect.
Frame
Name the decision or workflow, its owner and the success test. Confirm the data and control requirements before any build.
Signed frame
Sprint
A 2 to 4 week sprint on a live case, ending in a working prototype the owner can use and the evidence behind it.
Working prototype
Production
Move into the workflow under the governance framework: integration, identity, logging, monitoring and change control.
Production system
Transfer
Hand over the method, roles and runbooks so your team runs the next one.
Handover pack
Outcomes
What changes when it works.
Outcomes are measured against a baseline agreed at Frame. We do not report a number the project did not measure.
Growth
A decision reaches a conclusion faster, or a sourcing funnel widens.
Time to conclusion · Candidates reviewed · Decisions taken
Efficiency
A workflow runs with less manual handling and fewer breaks.
Straight-through rate · Handling time per item · Exceptions per hundred
Risk and control
The control environment improves, and the evidence is there when it is asked for.
Named owner per output · Audit trail completeness · Open findings
Proof
What a decision record looks like.
Macro and rates
The engine we refused to build
A proposed factor-attribution engine was narrowed to one testable claim. The rest was refused with a reason, and the record says why.
Read the recordMarket infrastructure
120x from a better question
A measured optimization programme replaced a scan with a constant-time test, and reverted a 5.2x regression instead of shipping it.
Read the recordCommodity flows
What a flow can and cannot tell you
Physical links to price, registered as predictions before the outcome. A work in progress, stated as one.
Read the recordGoverned by design
Every output carries its evidence trail.
Five controls, each with an artefact your risk function can inspect. The control is shown, not asserted.
Read the security posture →Approved data only
If a source is not approved, the job cannot reach it. Models and tools sit on the same allow-list.
Identity on every request
Every job resolves to the person who started it. Agents inherit that identity, not a broader service account.
Audit trail to sign-off
Which model, which sources, what was written, and who reviewed it. Reconstructible after the fact.
A named owner
One person signs each conclusion. Accountability is not delegated to a model.
Containerized sandbox
Delivered in a containerized environment your risk function can inspect: isolation, data location, access, logging and exit.
The engineering
The systems behind the practice.
The products are evidence that we build and run real systems. They are the substrate for delivery, not the offer.
AI traffic security
mGateway
Network-layer security and policy for AI traffic. Every request resolves to a person, every model and tool call follows the allow-list.
Visit site →AI coaching
MindCoachLabs
Decision coaching grounded in research-backed frameworks. A separate product line for individual and team development.
Visit site →Agent work loop
Harness
The loop behind the practice: research, plan, build, verify, and the evidence trail that makes the work inspectable.
Visit site →Nine methods in development, from red-teaming to market discovery. See Labs.
Next step
Bring one decision or workflow.
A short sprint, a working prototype, and a method your team can run next. We start with the decision, its owner and the success test.