We build agents, small language models and AWS Bedrock solutions for banks, fund administrators and asset managers. Every step is logged, every result can be reproduced, and a person signs off where it matters.
09:14:02 · sha256 4e1c…a90bcapital_call · confidence 0.9718 fields · model slm-fin v2.342 passed · 1 flagged for review09:16:40 · logged to audit trailOne team that takes AI from a base model to a system running inside your controls. We design it, build it, and stay to run it.
Task-specific agents that read documents, query data, call your systems and hand off to people. We orchestrate multi-step work with explicit state, so you can see why an agent did what it did.
Compact models fine-tuned on your domain and hosted in your environment. Lower cost per call, predictable latency, and no client data leaving your boundary.
Production workloads on Amazon Bedrock inside your AWS account: model selection, guardrails, retrieval, cost controls and monitoring, set up the way your security team expects.
A shared foundation so each new use case doesn't start from zero: a sandbox to try ideas safely, a path from sandbox to production, single sign-on, and a gateway in front of every model.
When the problem doesn't fit a product, we build what does. Short discovery, a working prototype on your data, then a hardened system with the documentation your risk team will ask for.
Work that is repetitive, rule-bound and expensive to get wrong. That's where agents with proper controls pay back fastest.
Capital calls, statements, KYC packs, contracts and trade confirmations classified and extracted, then checked against your rules. New document types are added by configuration, not a fresh engineering project.
Plain-English questions turned into governed SQL over your warehouse, with the query shown alongside every answer.
A reusable rules engine for validation, reconciliation and compliance checks, with mappings owned by your IT team.
Agents review communications, trades and filings against policy and raise flagged items with the evidence attached.
Drafts of commentary, factsheets and investor responses grounded in approved data, ready for a person to edit and sign.
Answers over internal policies, filings and research, each one cited to the source paragraph.
Risk and compliance teams don't need to trust the model. They need to see what it did. These controls ship with every system we deliver.
Small steps, each with something you can see and judge before committing to the next.
Two weeks with your team to pick one process, measure today's effort and error rate, and agree what success looks like.
A working agent in a sandbox on a sample of your real data. You see results, failure cases and cost per run.
Controls, integrations, security review and evaluation suites added. Documentation written for risk and audit.
Live in production with monitoring and a named team. We tune, retrain and extend to the next process.
Production-grade, cloud-agnostic and built as microservices, so each piece can be scaled, swapped or audited on its own.
Hosted foundation models in your cloud, or open models we run for you.
AWS Bedrock
Microsoft Azure
Hugging Face
OllamaOrchestration, prompt versioning, tracing and evaluation.
LangGraph
LangChain
Langfuse
OpenTelemetryRelational, graph and embedding stores for retrieval and memory.
PostgreSQL
Qdrant
Pinecone
Neo4j
Redis
ElasticsearchContainerised services behind a gateway, deployed on Kubernetes with full observability.
Kubernetes
Docker
Helm
Kong Gateway
Keycloak
RabbitMQ
Apache Kafka
Python
FastAPI
Terraform
Prometheus
Grafana
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Tell us which workflow costs your team the most time. We'll come back with a view on whether an agent can help, what it would take, and how you'd control it.