Service · Design & build
The gap between a working demo and a system your compliance function will approve is mostly architecture. This is the part we have done repeatedly inside banking environments, where getting it wrong has consequences.
A prototype answers the question "can this work?" Production answers a harder one: what happens on the day it's wrong, and can you show anyone why it did what it did?
Our team has designed and launched enterprise LLM gateways with built-in guardrails, low-code generative AI platforms used by non-technical staff, internal marketplaces for AI agents, and intelligent document processing pipelines — all within financial institutions where model access, policy compliance and auditability were preconditions rather than nice-to-haves.
A single governed entry point to language models — commercial, open-weight, or both. Policy enforcement, prompt and response logging, cost attribution by team, and the ability to change model providers without rewriting every downstream application.
Input and output filtering, PII detection and redaction, topic and tone constraints, and escalation paths for the cases the system should refuse. Designed so that policy lives in configuration your compliance team can read, not buried in application code.
Document ingestion, chunking strategy, embedding and index design, and retrieval quality tuning. Most disappointing RAG systems are not failing at generation — they are failing at retrieval, and the fix is unglamorous engineering rather than a better model.
Multi-step automation where a model plans and calls tools, with the constraints, approval gates and observability that make it something you can put in front of a real process rather than a demo.
The component most often skipped and most often regretted. A repeatable test suite that measures output quality against a golden dataset, so you can tell whether a prompt change, model upgrade or data refresh has made things better or quietly worse.
Deployment pipelines, monitoring, drift detection and model governance documentation — the operational layer that turns a project into a system somebody owns on Monday morning.
A first production use case typically runs six to eight weeks from a completed readiness assessment. Larger architecture and implementation programmes run longer, and across the boutique market these typically sit in the $150,000 to $400,000 range for a full build with ownership transferred — against $500,000 and frequently above $1M for the same scope at an enterprise firm.
Everything runs on your accounts, in your cloud, under your credentials. If a component of the architecture only works while we are engaged, we have built the wrong thing.
For clients under FCA, SEC, DNB or comparable supervision, the documentation a regulator or internal audit function will want is produced as the system is built, not reconstructed afterwards from memory. That includes model risk documentation, data lineage, decision logging, and a clear account of where a human remains in the loop.
This is the single biggest reason mid-market regulated firms stall between pilot and production, and it is the part of the work our team has the deepest experience in.
Start here
If you already know the use case, we can usually tell you within one conversation whether the architecture is straightforward or whether the hard part is somewhere you haven't looked yet.