SOLUTION · FINANCIAL SERVICES
Financial Services
Faster decisions that regulators can still follow. Auditable, fully traceable processes with VPC or on-prem deployment, turning document review and risk analysis from days into hours.
Move faster without losing the audit trail
For financial institutions, the question was never whether AI can do the work, it's whether you can hand the output to compliance and audit with a straight face. Due diligence, contract review, and KYC/AML screening pile up hundreds of documents, and analysts spend far more time pulling files, hunting clauses, and cross-referencing than actually making the call. Bring in a black-box model and you trade that for a different problem: you can't tell the regulator why it reached that conclusion. Accuracy, explainability, and data residency end up feeling like a pick-two.
Tenten AI is an AI-native product studio that works the FDE way: we embed engineers inside your environment and sit down with compliance, risk, and IT to break the workflow into verifiable steps, rather than selling you a generic tool. Every AI conclusion carries the source passage, the cited page, and the reasoning trail behind it, so an analyst can see it, audit can check it, and a regulator can trace it back. The whole system runs in your VPC or on-prem, and the data never leaves your domain.
We use RAG to anchor the model to your own documents and policies instead of letting it improvise, and we govern model risk with human-review gates, confidence scores, and reproducible audit logs. Delivery moves in weeks, not quarters: you typically have a working MVP running on real files within 2 weeks, not a PoC slide deck.
Capabilities
01
Fully traceable audit trail
Every conclusion links back to the exact source passage, page number, and timestamp, and we preserve the model's reasoning steps so compliance and audit can review each item and reconstruct the full decision path for regulators.
02
VPC or on-prem, data stays home
The entire system deploys inside your private cloud (VPC) or data center, keeping documents, vector indexes, and model calls within your network boundary to satisfy data-residency and cross-border transfer requirements.
03
Document review and risk analysis in hours
The repetitive extraction, comparison, and flagging work in due diligence and contract review runs automatically, so a several-hundred-page file gets a first pass in hours and analysts focus on judgment and exceptions.
04
RAG anchored to your policies
Retrieval-augmented generation binds the model to your own clause library, internal policies, and historical cases, with every answer citing its source, which shuts down fabrication and lowers model risk.
05
Human-review gates and confidence scores
High-risk items are flagged with a confidence score and routed for human sign-off, establishing an AI-drafts, human-decides workflow so the model never issues a final ruling unsupervised.
06
KYC/AML screening and exception handling
Beneficial-ownership structures, sanctions lists, and adverse media are screened automatically, with hits packaged into reviewable cases that free analysts from drowning in false positives.
Use cases
M&A due diligence
Ingest the hundreds of contracts, financials, and disclosures in a data room at once, auto-extract key terms, surface change-of-control, non-compete, and contingent-liability clauses, and output a cited risk list for counsel and the deal team to review.
Contract and clause review
Benchmark against your standard clause library to flag terms that deviate from standard, missing protective covenants, and unfavorable language, with side-by-side comparisons and suggested redlines.
Credit and investment risk analysis
Pull metrics from financial statements, credit reports, and industry data to produce a structured risk summary and red-flag list, with every figure traceable back to its source.
KYC/AML customer due diligence
At onboarding and periodic review, automatically compile beneficial ownership, sanctions and PEP screening, and adverse media into a traceable case file flagging items that need a second look.
Regulatory inquiries and evidence response
When a regulator or internal audit asks, quickly retrieve supporting evidence from policy documents and case records and draft a cited response, cutting the time it takes to substantiate a position.
Delivery cadence
WEEK 1
Embed and map the workflow
Engineers embed and, alongside compliance, risk, and IT, pick the first high-value workflow, inventory document types, data-residency requirements, and audit standards, and define verifiable success metrics.
WEEK 2
MVP on real files
Deploy the RAG pipeline and review interface in your VPC or on-prem environment and produce a first working result on real (de-identified) files, not a slideware PoC.
WEEK 3-4
Harden for audit and tune
Tune prompts, citation granularity, and confidence thresholds against analyst and audit feedback, complete the audit trail and access controls, and stand up the human-review gates.
WEEK 5+
Go live and scale
After compliance sign-off, go live and extend the same framework to other lines of business, with operations runbooks and knowledge transfer to your internal team.
Hours not days
Document review and risk analysis cycle
100%
Conclusions traceable to source
VPC / on-prem
Deployment, data never leaves your domain
FAQ
Will the AI's conclusions hold up to audit and regulatory review?
Yes. Every conclusion carries the source passage, cited page, and reasoning trail, so compliance and audit can review each item and reconstruct the full decision path. We break the workflow into verifiable steps rather than black-box output, so you can clearly explain to a regulator why the model reached its conclusion.
Does our data leave our environment?
No. The whole system can deploy in your VPC or on-prem data center, keeping documents, vector indexes, and model calls within your network boundary. That lets you meet data-residency and cross-border transfer rules, so sensitive files never get shipped to a third-party cloud.
How do you control model risk and hallucination?
We anchor the model to your own documents and policies with RAG, and every answer cites its source, which shuts down unsupported fabrication. High-risk items are flagged with a confidence score and routed for human review, creating an AI-drafts, human-decides split so the model never makes a final ruling unsupervised.
Which models can we use, and can we choose?
Yes. We are model-neutral and can connect Anthropic, OpenAI, or on-prem open-source models inside your environment, chosen to fit your data-residency and cost requirements. All model calls happen within your deployment boundary.
How fast do we see real results?
You typically have a working MVP running on your real (de-identified) files within 2 weeks, not a PoC slide deck. The first high-value workflow usually reaches compliance sign-off in 4 to 6 weeks, after which we extend the same framework to other lines of business.
Will this replace our analysts?
No. The goal is to free analysts from the repetitive file-pulling, comparison, and flagging so they focus on judgment and exceptions. The system drafts and organizes, while final decision authority and the review gates always stay in human hands.
A new era of
AI-native products
Ship your first AI use case in weeks, not quarters.