For Financial Services
Pokee deploys RL-trained AI agents on your terms — on-prem, on-device, or inside your VPC — so investment, risk, and back-office teams can move at the speed of their data without ever leaving the perimeter.
Token context window — single GPU, no chunking required
Private deployment — model weights and data never leave your perimeter
Agents trained with reinforcement learning, not prompt-engineered chains
Three capabilities, one engine
Pokee gives front-, middle-, and back-office teams the same set of primitives — research, reason, and act — wired into the systems where finance actually happens.
Surface auditable answers from filings, transcripts, market data, and proprietary desks — grounded in citations your compliance team will actually accept.
Agents trained with reinforcement learning — not stitched-together prompts — to plan across long horizons, fall back gracefully, and stay within policy.
Deterministic tool use across your stack — Bloomberg, OMS/EMS, ERPs, ticketing, ledgers — with full audit trails for every API call an agent makes.
Use cases
Five places Pokee agents are already replacing brittle scripts and outsourced labor inside banks, asset managers, and accounting platforms.
For analysts and PMs who can't afford to wait for the next analyst note.
Replace the offshore queue with agents that read, classify, and reconcile.
Synthesize structured signals and unstructured signal noise in one place.
For compliance teams shipping under the ever-shifting weight of new rules.
The data-sensitive support work generic LLMs simply cannot touch.
Inside the Pokee Engine
The Pokee Engine is our proprietary inference architecture — a black box for now — that pushes context windows past ten million tokens on a single GPU. No chunking, no retrieval gymnastics, no lossy summaries. The whole portfolio fits in memory.
Verified, not vibes
FinanceBench is the industry-standard evaluation built on 150+ real 10-Ks, 10-Qs, and earnings transcripts. On the headline Oracle test — the configuration that mirrors a production RAG pipeline — Pokee outscores GPT-5.4 outright.
Headline · Oracle accuracy
On the Oracle test — where the model is given the relevant document and must extract, compute, and reason — Pokee edges out the frontier model by +0.4 points. This is the configuration that maps to a real production RAG pipeline, and it's the one we lead with.
Extraction is the dominant workload in enterprise RAG — and the one that decides whether your 10-K agent ships or stays in pilot. Pokee 93.9% vs GPT-5.4 90.9%.
On the long-context aggregate, GPT-5.4 edges Pokee by 1.7 points — driven by the logical-reasoning subset, which represents under 10% of real finance workloads.
Pokee delivers these scores on a single workstation-class GPU inside your VPC. GPT-5.4 doesn't ship that way — at all.
Built for the regulated stack
Pokee was designed from day zero to live behind your firewall. We don't ship a SaaS that pretends to be enterprise — we ship infrastructure your security team can actually own.
Run inside your VPC, on-prem datacenter, or directly on workstation silicon. Weights and data never leave your perimeter.
Every agent action is logged, traceable, and replayable. Built-in usage monitoring, output auditing, and policy guardrails.
Fine-tune on your tickets, memos, and ledgers. RL-based adaptation means policy changes ship without retraining from scratch.
Pokee engineers embed with your team for the first deployment. We ship to production — not slides — inside the first quarter.
Get started
Connect with our team to scope an agent that fits your stack, your data, and your regulator's appetite for risk.
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