Services
Two sides of the same table.
We sell into banking for AI companies, and we deliver inside banks for the institutions themselves. Working both sides is the point — it is why our advice on either side is not theoretical.
Go-to-market for AI in banking
For AI companies, model providers and platform vendors who need to win regulated financial institutions — and keep them past the pilot.
Where we come in
Most AI companies arrive at banking with a horizontal product and a demo that lands well with innovation teams and dies in second-line review. The work is translating capability into something a risk, compliance, procurement and business-line audience can each say yes to — in their own language, in the order they need to hear it.
- Segmentation and ICP. Which institutions, which size tier, which function, which buying centre. Tier-1 global bank and a $4bn regional are not the same market and should not get the same motion.
- Positioning and narrative. Reframing from "we use LLMs" to a named business problem with a defensible before-and-after.
- Procurement and risk readiness. Security questionnaires, third-party risk packs, model documentation, data flow diagrams, SOC 2 / ISO gaps, DPA and exit-plan language — prepared before the buyer asks.
- Pilot and proof-of-value design. Baselines measured up front, a scoped success metric, a named business owner, and a written path from pilot to production on day one.
- Pricing and commercial structure. Per-seat, per-decision, outcome-linked or platform fee — and how each survives a procurement negotiation.
- Reference architecture. In-tenant, VPC, on-prem and hybrid deployment patterns, so the architecture review is a conversation and not an ambush.
- Channels and partnerships. Core providers, systems integrators, consultancies and cloud marketplaces — the routes that shorten a two-year cycle.
- Pipeline execution. Warm introductions, RFI/RFP response, analyst and conference positioning, and sales-team enablement so it does not depend on us.
Consulting & engineering resources
For banks, credit unions, insurers, asset managers and payments companies that have the mandate and need the bench.
What you get
Senior, named people who have shipped inside regulated environments — embedded with your teams, working in your tooling, under your controls. We staff small and deep rather than wide and junior, and we are explicit about handover from the first week so capability stays with you.
- AI strategy and use-case portfolio. A ranked backlog scored on value, feasibility, data readiness and regulatory exposure — not a list of everything possible.
- Solution architecture. Model selection, RAG and retrieval design, orchestration, evaluation harnesses, guardrails, cost modelling, and integration with core banking, CRM and data platforms.
- Data and platform engineering. Pipelines, entity resolution, feature and vector stores, lineage, and the unglamorous data work that decides whether any of it works.
- MLOps and LLMOps. CI/CD for models and prompts, versioning, monitoring, drift and regression detection, incident runbooks.
- Evaluation and assurance. Golden datasets, offline and online evals, red-teaming, bias and responsible lending testing, and the documentation your model risk function will ask for.
- Governance enablement. Model inventory, validation-ready documentation, and AI policy that maps to existing frameworks rather than replacing them.
- Build and run. Production delivery of copilots, document intelligence, decisioning support and agentic workflows — plus the support model after go-live.
- Team uplift. Pairing, code review, internal enablement sessions, and a defined exit so your engineers own it.
Roles we place
Who actually shows up.
AI solution architects
End-to-end design, vendor-neutral, fluent in your control environment.
ML & LLM engineers
Retrieval, fine-tuning, agent orchestration, latency and cost optimisation.
Data engineers
Pipelines, quality, lineage and the integration layer into core systems.
MLOps / platform
Deployment, observability, model and prompt lifecycle management.
Evaluation specialists
Benchmarks, golden sets, red-teaming, regression gates before release.
Model risk analysts
Validation-ready documentation and second-line translation.
Banking SMEs
Lending, payments, AML/KYC, treasury, servicing and collections.
Engagement leads
One accountable person, weekly demos, no status-report theatre.
Engagement models
Buy the smallest thing that answers your question.
Every engagement is designed to end with a decision or working software. We will tell you which of these fits, including when it is the cheapest one.
| Model | Typical length | Best for | You end with |
|---|---|---|---|
| Diagnostic sprint | 2–3 weeks | Deciding where AI is worth trying at all | Scored use-case portfolio, data readiness assessment, one recommended first build |
| GTM engagement | 3–6 months | AI companies entering or scaling in financial services | Positioning, procurement-ready pack, pilot playbook, qualified pipeline, enabled sales team |
| Embedded pod | 3–12 months | Institutions building production AI without a standing team | Shipped system, documentation, and your team able to run it |
| Fixed-scope build | 6–14 weeks | A single well-defined workflow or integration | Deployed workflow, evaluation results, support handover |
| Named specialists | Rolling | Filling a specific gap in an existing programme | Senior individuals inside your team, your process, your tooling |
| Advisory retainer | Ongoing | Boards, CIOs and product leaders who need a second opinion on tap | Architecture reviews, vendor evaluations, roadmap challenge sessions |
How an engagement runs
Four steps, no surprises.
Frame
One week. We define the problem in business terms, agree what success is worth in money or hours, and find the baseline. If there is no measurable baseline, that is the first finding.
Prove
Two to six weeks. Smallest credible build against real data, with an evaluation harness from day one. Compared against the baseline and against the boring alternative.
Harden
Controls, monitoring, documentation, failure modes, human-in-the-loop design, and the model risk and third-party risk artefacts. This is where most projects stop and where value actually starts.
Hand over
Runbooks, training, and a named owner on your side. We stay only for the parts you want us to stay for.