Bring governed AI into the everyday work of banking.
AI prepares the work. Your people make the decisions. cognipeer helps banks and financial services providers build AI workflows for compliance, operations, customer service and technology teams, with access rules, approval points, guardrails and tracing.
Banking AI Peer
Approved sources and records
Regulations
Contracts
Controls
Owners
Audit team asks
Check the processes in this audit scope and list the gaps process owners should close first.
Assistive next actions
Put AI inside the bank’s workflows, not beside them.
Banking work runs on policies, contracts, regulatory bulletins, customer records and control evidence. cognipeer connects AI to the sources each workflow needs, so it can prepare a summary, a structured record or a prioritised list for the person who owns the next step.
Contract under review
Authorised viewDocument text
Clauses highlighted for the first review
Key clauses
Points needing attention
Review teams
Current step
First review prepared
Decision
Stays with the review team
Banking AI Peer
Approved sources and records
Highlight the key clauses in this contract and anything that needs attention.
I have highlighted the key clauses, the points that need attention and the elements to summarise. The review and every decision stay with your team.
AI works better when documents, records and owners are connected.
Bring together the sources each banking workflow depends on, so AI can prepare the work and pass it to the person who owns the next step.
Regulatory bulletins
Contracts & legal documents
Forms & applications
Policies & procedures
Control records
Customer records
Knowledge base
Project tracking
Reduce the manual preparation around regulated banking work.
In many banking workflows, information has to be found, read, re-keyed or compiled before the real assessment or decision can begin. That preparation is where AI can help first.
Regulatory change reaches teams late
New rules have to be found, read, interpreted and passed to the right teams before anyone can assess them.
Audit gaps surface at the last minute
Missing documents, open actions or outdated procedures can come to light just before an audit, leaving little time to close them.
Documents are read and re-keyed by hand
Fields and clauses are picked out manually, and initial assessments can vary from one review to the next.
Knowledge is scattered and goes stale
Answers sit across policies, guides and separate records, and frequently used content can quietly lose its accuracy.
Build banking AI around the work your teams already do.
Connect the sources each workflow needs, then shape the AI Peer around the output your teams review and the decisions they keep.
Discuss your banking workflowStart compliance work sooner
Monitor designated sources, summarise new rules, flag gaps before audits and highlight key clauses for a first review. Experts own the assessment.
Turn documents into structured input
Extract the fields, information and clauses each process needs, in the same format every time, ready for the next step.
Keep knowledge and reporting current
Flag outdated content for its owner and send managers a daily summary without manual reporting.
Help customers and relationship teams
Guide customers to the right screen, ground support answers in approved knowledge and bring customer records together for sales teams.
Banking AI use cases from real customer projects.
Every use case below comes from an anonymised customer project and links to the full case study. Start with the one closest to your own work.
First passes on regulation, audits and contracts. Specialists keep the assessment.
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Regulatory change pre-assessment
Monitoring of official sources with summaries, impact classification and suggested review teams, while experts own the assessment.
Read the case study -
Internal audit preparation
Early scanning of documents, previous actions and control records before internal audits, with prioritised lists for process owners.
Read the case study -
Legal and contract review
Key clauses and points needing attention highlighted for a faster first review, with the assessment left to the bank’s teams.
Read the case study
Structured output from documents and a knowledge base that stays current.
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Document data extraction
Required fields and critical clauses extracted from contracts, forms and applications as structured output for the next step.
Read the case study -
Outdated knowledge base content
Procedures, policies and help content checked for signs of being out of date, with revision requests raised for content owners.
Read the case study
Guidance for customers in digital channels and a clearer view of each customer for sales teams.
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Customer guidance in digital channels
Natural-language help inside a bank’s digital channels, pointing customers to the right screen and the next step.
Read the case study -
Sales and CRM assistant
Customer details, meeting notes and follow-up actions brought together in short summaries for a bank’s sales teams.
Read the case study
Daily summaries and clearer planning for IT and software teams.
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Daily technology team summaries
End-of-day manager summaries of opened and closed work, open items and critical deviations, prepared without manual reporting.
Read the case study -
Software planning
Needs, issues and technical records summarised and broken into work items, giving software teams a clearer view for planning.
Read the case study
AI prepared the work. The bank’s people made the decisions.
Anonymised customer projects built with cognipeer Studio. In each one, AI did the preparation and the bank’s own teams kept the assessment and the decision.
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Banking
AI Pre-Assessment of Regulatory Changes for a Bank
What AI didFollowed the official sources and bulletins the bank designated, summarised each new regulation, classified potential impact areas and suggested which teams should review it.
What stayed with peopleRead the case studyThe impact assessment and the bank’s response, owned by its legal, compliance, finance and business teams.
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Banking
How a Bank Used AI to Identify Gaps Before Internal Audits
What AI didScanned documents, previous actions and control records against the audit scope, flagged gaps early and produced prioritised preparation lists.
What stayed with peopleRead the case studyClosing each gap, owned by process owners. The audit itself stayed with the internal audit team.
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Banking
AI-Assisted Legal and Contract Review at a Bank
What AI didHighlighted key clauses, points needing attention and elements to summarise, giving every review the same starting point.
What stayed with peopleRead the case studyThe review, any comparisons and every decision, owned by the legal, contract management, procurement and compliance teams.
Our case studies describe real customer projects and are anonymised.
Ask once. Bring the right banking context together.
A banking AI Peer can check the sources your bank designates, summarise what changed and suggest who should review it. The assessment and the response stay with your experts.
Banking Peer
AI agent · Designated sources and owners
What changed in this week’s bulletins, and who should review it?
Connected sources checked
A new regulation was detected in the designated sources. Here is a short summary, the areas it could affect and the teams suggested for review. Open review tasks for them?
Yes, open review tasks for the suggested teams.
Done. Review tasks are open for the suggested teams. The impact assessment and the bank’s response stay with them.
Connect AI to your bank’s systems, inside your controls.
Build banking workflows in Studio and manage models, guardrails, tracing and usage in Console, the self-hosted AI gateway and control plane. Decide where cognipeer runs, what each Peer can access, where people approve and what is recorded.
Discuss your deployment and controlsYour knowledge and systems
Ground Peers in documents, websites, APIs and databases, and connect tools that update the systems your teams use.
Deployment options
cognipeer supports cloud, private and self-hosted environments, according to your architecture requirements.
See enterprise deploymentGuardrails and personal data
Console guardrails can detect personal data such as credit card numbers and IBANs, then block, mask, warn or flag it before a model call and before an answer is returned.
Tracing and audit
Console traces agent executions, tool calls and model requests, and produces trace events, audit logs and usage exports. It can be configured to support your audit and policy requirements.
Access and approvals
Set data access, tool permissions and approval points for each use case in Studio. Scope API keys and quotas by project in Console.
Model choice
Choose the model for each Peer or project. Console offers an OpenAI-compatible API with routing and fallback across providers.
cognipeer supports the preparation work. Compliance, legal, audit and customer decisions stay with the bank’s accountable teams.
Start with one banking workflow, then expand.
Begin with a workflow that has a clear owner and a lot of manual preparation, then expand into related banking workflows as your teams gain confidence.
01
Choose a workflow with a clear owner
For example regulatory pre-assessment, audit preparation or document extraction. Agree the sources it uses and who decides.
02
Define the Banking Peer
Set its sources, outputs, limits and review steps, and test it with evaluation suites before rollout.
03
Connect approved actions
Add the handover, such as review tasks, revision requests or summaries for the right team, then expand to the next workflow.
Questions banks ask before they start.
Short answers on deployment, decisions, tracing and models. Bring any other questions to a demo.
Can cognipeer run inside our own infrastructure?
Yes. cognipeer supports cloud, private and self-hosted environments. Console runs inside your own infrastructure, and Studio offers an on-premise option.
Does the AI make decisions on its own?
You decide where approval points sit. Studio workflows can include user approval steps, and gated actions pause for a human decision. In our banking case studies for internal teams, AI prepared summaries, structured output and prioritised lists, and the bank’s own people made the decisions.
Can we trace what the AI did?
Yes. Console traces agent executions, tool calls and model requests, and provides trace events, audit logs and usage exports. It can be configured to support your audit and policy requirements.
Which models can we use?
Choose the model for each Peer or project. Console provides an OpenAI-compatible API with routing and fallback across providers.
How do we start?
Start with one workflow and a clear owner. We help with use case design, knowledge and model design, integration and rollout. Book a demo and bring the workflow you want to start with.
Put governed AI to work across your bank.
Build banking AI Peers that prepare the work, keep decisions with your people and run within your controls. Book a demo and bring one workflow you want to start with.