Field guide
AI architecture for finance
Retrieval, agents and evaluation, designed around the constraints finance imposes.
A finance-AI system is not a model with a prompt in front of it. It is a set of decisions about where retrieval ends and generation begins, how much autonomy an agent gets, what every call costs, and how a person checks the answer before it reaches a client or a regulator.
This guide collects how we think about that architecture: hybrid retrieval over financial documents, where agentic autonomy earns its keep and where it does not, and the evaluation that tells you whether any of it is good enough to ship.
Current signals
- Agent stacks now treat tools as governed APIs, with authorization, rate limits and action validation before anything executes.
- Hybrid retrieval is the baseline; GraphRAG is used where entity relationships matter more than semantic similarity.
- Evaluation-first is standard: teams test full trajectories, tool choice, latency, cost and security in CI and production, not one-off demos.
- Deterministic code still owns arithmetic and transactions; model calls are reserved for reasoning, retrieval and classification.
In this guide
When the document is the attacker: securing RAG in finance
Retrieval pulls semi-trusted filings and emails straight into the prompt. Here is how we treat that content as untrusted and build a layered defence for finance AI.
ReadBatch or real-time? Inference patterns for finance AI
Not every score needs to be computed at request time. Here is how we split batch precompute from real-time inference to hit cost and latency targets.
Making function calling reliable in finance workflows
A model that calls the wrong tool with the wrong arguments is a production incident. Here is how we make function calling dependable in finance systems.
An LLM gateway for access, cost and governance
Ungoverned model access is a cost and compliance leak. Here is the gateway we put in front of providers for keys, quotas, logging and policy.
Long context or retrieval? Choosing for finance documents
Bigger context windows do not retire retrieval. Here is how we decide between stuffing the context and retrieving, for filings, contracts and statements.
Where small language models beat frontier models in finance
Not every finance task needs a frontier model. Here is where a fine-tuned small model wins on cost, latency and control, and where it does not.
Eval-driven development for finance AI
Write the eval before the feature. Here is how we run finance AI like test-driven development, with a scored eval set gating every change.
Caching strategies for LLM systems in finance
The cheapest model call is the one you do not make. Here are the prompt, semantic and result caches we use to cut finance LLM cost and latency safely.
Governing agent tool use in finance workflows
An agent that can call tools can move money. Here is the authorization, rate-limiting and action-validation layer we require before an agent touches a finance system.
On-prem, VPC or API: hosting AI in regulated finance
Where the model runs is a compliance decision as much as a technical one. Here is how we weigh API, VPC and self-hosted models for regulated finance data.
Multi-agent orchestration in finance, and when to avoid it
One capable agent beats a swarm more often than the demos suggest. Here is where we split work across agents in a finance system and where a single loop is safer.
Reranking: the retrieval step most finance RAG skips
First-pass retrieval returns plausible passages; reranking returns the right one. Here is how we add a reranker to finance RAG without blowing the latency budget.
A reference architecture for finance AI systems
The same shape recurs across finance AI builds. Here is the reference architecture we start from: data layer, retrieval, model, guardrails, human review and audit.
Model routing and fallback for cost and reliability
One model for every call wastes money and breaks under load. Here is the routing, cascade and fallback design we use across a finance workload.
Output validation patterns for finance AI
A confident wrong answer is the failure mode that matters. Here are the schema, constraint and verification layers we put between a model and a finance workflow.
Where agentic AI belongs in finance, and where it does not
Agentic AI is spreading fast across finance teams. The useful question is not whether to adopt it, but which decisions you let an agent make alone and which a person signs.
LLM observability and tracing for finance workloads
You cannot debug what you cannot see. Here is the tracing, logging and evaluation-in-production setup we use to keep a finance LLM system diagnosable.
Building a RAG evaluation harness for finance
Vibes are not an eval. Here is the retrieval + generation test harness we stand up so a finance RAG system has a number that moves before it ships.
Measuring AI ROI in finance operations: the metrics CFOs accept
Hours saved is not a business case. Here are the payback, quality and risk metrics we use to prove a finance-ops AI system earns its keep in production.
Hybrid retrieval for filings, transcripts and analyst reports
Pure vector search loses the exact tickers and defined terms that financial answers hinge on. Here is how we build hybrid RAG that an analyst can trace back to the source line.
Build vs buy for finance-ops AI: a decision framework and TCO model
Point tool, platform or engineering-led build? Here is the framework we give finance and risk leaders to decide, including the total-cost and control trade-offs.
Choosing a model for finance workloads, starting from the task
A leaderboard cannot tell you which model fits your finance workload. The task and an eval set can. Here is how we pick, and what the tradeoffs actually cost.
Turning "it looks good" into a number before a finance AI system ships
A finance AI system needs a defensible accuracy number before it goes live. Here is the eval set we build, the baseline we hold it to, and why audit cares.
Controlling LLM cost when finance asks per-query
An LLM bill that looks fine in aggregate hides where the money goes. Here is how we keep per-query cost low enough to survive a CFO's questions.
Making LLM output safe to feed a finance system
A free-text number from a model should never flow straight into a ledger. Here is the method we use to make LLM output reliable enough to act on.
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