Generative AI grounded in your data, not making things up
We build knowledge assistants, copilots, document extraction, and custom models for fintech and SaaS teams. Model-agnostic, grounded in your sources with retrieval, and private by design. Shipped in weeks, not quarters.
Book a generative AI discovery callNDA on request. Your data stays yours.
Which model is right for you? →25+
Product
200+
Engineers
5 Days
Embeds
Trusted by fintech teams across the US, UK, UAE, and Singapore
What we build, grouped by what you are making
This buyer self-identifies by what they are shipping. Each card is one concrete capability.
Knowledge assistants
Answers grounded in your documents, wikis, and data, with citations, so users can trust the output.
Copilots in your product
An assistant embedded in your app that helps users do the thing your product is for, faster.
Document and data extraction
Turn contracts, invoices, forms, and reports into structured, validated data.
Content generation
On-brand drafts, variations, and summaries at scale, with a human approving the output.
Custom and fine-tuned models
When a base model is not enough, we fine-tune or build for your domain, data, and latency.
AI features in your product
Search, classification, recommendations, and natural-language interfaces, added to what you already ship.
First, which model, and grounded how?
Every generative project turns on two choices, and most vendors pick for you to suit themselves. We pick for you, based on your data, privacy, latency, and budget.
OpenAI, Anthropic, Google
Fastest path, strongest reasoning
Provider API, with no-training terms
Per token
Less control, ongoing per-call cost
Llama, Mistral
Privacy, cost at scale, control
Your cloud or VPC
Infra-based, cheaper at high volume
You run the infra
Custom
Narrow domain, strict latency or format
Your cloud or VPC
Higher upfront, cheaper per call
Build effort and upkeep
Keeps answers current and cited without retraining. The default for most production systems.
Bakes in tone, format, or a narrow skill. We add it only when RAG alone cannot hit the bar.
Use RAG, and add fine-tuning where needed. We recommend the combination during discovery, based on your data, privacy, latency, and budget.
How we stop it from making things up
Retrieval-augmented generation is how a model answers from your data instead of its training memory. We chunk and embed your sources, then store them in a vector database (Pinecone, Weaviate, Qdrant, or pgvector). For each question we retrieve the most relevant passages, rerank them, and give the model only what is relevant. The result is answers your users can verify, not confident fiction.
Instructed to cite, and to say I do not know rather than guess
Where your data goes, in plain terms
The question every serious buyer has and most agency pages skip. Plain, confident, no fearmongering.
When we use a hosted model, we configure no-training and no-retention terms so your data is not used to train anyone's model.
When privacy demands it, we run open-weight models in your own cloud or VPC, so data never leaves your perimeter.
Redaction and minimization before data reaches a model, with audit logging of what was sent.
Per-user and per-tenant access controls, so the assistant only ever sees what that user is allowed to see.
We build to your framework, working with your security team. Applicable frameworks confirmed before publication: [NEEDS-VALIDATION: founder].
From discovery to shipped feature
Most generative builds ship a useful prototype in 4 weeks and production in 6 to 12, depending on grounding complexity, model choice, and integration.
Discovery
We map your use case, data sources, model and grounding choice, and privacy needs. Output: a written spec and a recommended architecture.
Week 1Prototype
A working prototype on your real data, with evals so quality is measured, not guessed.
Weeks 2 to 4Harden
Grounding tuned, guardrails added, latency and cost optimized, integrated into your product.
Weeks 4 to 8Ship and improve
Monitoring, evals on every change, and refinement as your data and needs grow.
OngoingA sample of recent work
Scoped in 2 weeks. Shipped in parallel. Zero hours pulled from their core roadmap.
Embedded a dedicated team to ship an AI layer inside the client's existing stack. Built with evals, fallbacks, and monitoring from day one, integrated into their codebase and CI, documented and handed off clean.
From kickoff to clean report before their launch date.
Full audit of protocol contracts ahead of mainnet launch. Findings ranked by severity, fixes implemented and re-verified, final report delivered for investor and exchange due diligence.
References available under NDA. Anonymized is fine; we do not publish figures we cannot source. [NEEDS-VALIDATION: founder]
Frequently asked questions
We ground it in your data with retrieval and force it to cite sources. We also instruct it to refuse when it does not know, and measure accuracy with evals before it ships. Grounding plus evals is how you get answers users can trust.
It depends on your privacy needs, cost at your volume, and how specialized the task is. We compare hosted, open-weight, and fine-tuned options in discovery and recommend, rather than defaulting to one vendor.
No. We configure no-training and no-retention terms with hosted providers, and for strict cases we run models in your own cloud so data never leaves.
Yes. We deploy open-weight models in your cloud or VPC when privacy or cost calls for it.
Usually RAG first, because it keeps answers current and cited without retraining. We add fine-tuning only when RAG alone cannot meet the bar.
You do. NDA on request.
Tell us what you want it to do. We'll tell you how to build it right
Book a 30-minute discovery call. We will map your use case and data, recommend the model and grounding approach, address your privacy needs, and send a written architecture and plan. No vendor lock-in, no hype.
NDA on request. Your data stays yours.