SolutionsFinancial Services
Turn your onboarding and credit policies into AI agents
Trail parses your onboarding standards, credit policy, client files, and screening procedures into one governed context graph. Then AI agents run KYC/KYB, credit review, screening and periodic refresh on top of it — each answer carrying the clause, page and record it used.
Client onboarding
This week- 412cases in flight
- 58need evidence
- 24EDD triggered
- 88%auto-cleared
- KYB-20391Ardsley Trading Ltd.$250,000Cleared—
- KYB-20408Northgate Freight$1.2MScreening—
- KYB-20418Verity Mills Ltd.$400,000HeldD. Okafor
- KYB-20437Calder Health Group$780,000ApprovalS. Aliyev
- KYB-20452Pell & Rowe LLP$150,000Cleared—
Trusted by teams at leading enterprises
Top financial services teams
put Trail to work on:
Turn a compliance policy into a working agent — in one upload.
Import the onboarding standard, credit policy or suitability procedure your compliance team maintains. Trail reads it, builds the agent, and shows which clause produced each rule.
- 01
Import process docs
Drop in the onboarding standard, credit policy or exception procedure your team already maintains — PDF, DOCX, scans.
- 02
Agent auto-built
Trail parses them into a context graph. It outlines the agent's role, splits the process into phases, and adds the tools each step needs.
- 03
Review, approve, run
Every learned rule is explained and traced to the exact text it came from. Agents take decisions strictly based on these rules.
“…a complex or leveraged product may not be recommended to a retail client unless a completed appropriateness assessment is held on file and the risk disclosure has been acknowledged in writing within the preceding ninety days…”
If the client is retail and the product is complex, hold the recommendation until the assessment and a signed disclosure under 90 days are on file.
One graph underneath all your AI agents.
Everything Trail parses collapses into a single governed graph, and everything your compliance and credit teams automate draws from it, whether the work runs in autonomous agents or copilot chats.
Agents - run processes autonomously
- ObserveKYB-20418 | 3 owners · Onboarding standard §11.3
- ReasonOwner higher-risk. IDs 14 months old.
- ActionHeld for EDD. Refresh request sent.
- ObserveAlert SCR-7742 · Screening procedure §6.2
- ReasonPartial name match. DOB differs 9 yrs
- ActionDiscounted with rationale, logged
- ObserveFacility CR-4180 · Credit policy §8.5
- ReasonLeverage 4.1× — over the 3.5× ceiling
- ActionRouted to committee, clause attached
- ObserveQ3 cycle | 212 files · Refresh SOP §4
- Reason31 files past their 12-month refresh
- ActionEvidence requests issued to owners
Copilot Chats - ask questions and run tasks
Help your agents solve the most complex problems with context graphs.
A corporate onboarding shows a beneficial owner in a higher-risk jurisdiction on an entity with three owners, with identification documents 14 months old, and the relationship manager has asked to expedite — what's required before approval?
One question. Five kinds of context, each in a different system — the client record doesn't know the standard, and the standard doesn't know the passport is fourteen months old.
How the Context Graph works →Onboarding & risk policy
Policy context · What applies at this risk level?
Enhanced due diligence · refresh under 12mo
Client & entity records
System-of-record context · Which entity, which owners?
3 beneficial owners · 1 higher-risk
Document ages
Computed context · Are documents current?
14 months · two months stale
Relationship manager email
Conversation context · What's been requested?
Expedite requested · no exception filed
Exception procedure
SOP context · Who can waive it?
Compliance only, in writing
Connects to the agent platform you already use.
Point the agents your institution already runs, SAP Joule, Agentforce, Copilot, Claude or your own stack, at one Trail brain, through a native Model Context Protocol server, native retrievers, or a plain REST/GraphQL API. Add or switch platforms without re-teaching a thing.
35% of Fortune 500 and 10,000+ companies use Trail
Customer story · SaltPay
“Nanonets is like magic. I can't imagine how I would do invoice extraction without it.”
- Time vs. manual entry
- 99% saved
- Team productivity
- 10×
- Vendors covered
- 100K+ across Europe
- Posting to SAP
- manual → automatic
Teams using Trail see
- 95%Straight-through processing
- 80%Reduction in associated costs
- 15×Quicker turnaround times
Built for how modern
enterprises work.
Trail meets the highest industry standards for your security & compliance. We include all default controls that you'd expect, including SAML SSO, audit logs, IP allow-listing, data lifecycle management, and much more.
- SSO & SCIM
- SAML, OIDC, and SCIM user provisioning with Okta, Azure AD, Google Workspace.
- Role-based access
- Full RBAC and fine-grained access control across orgs, workspaces, agents.
- Audit logs
- Every agent run, approval, and data access is recorded, and streams to your SIEM.
- Private deployment
- VPC, single-tenant cloud, or on-prem. Choose your infrastructure and network policies.
- Data residency
- Pin data processing to US, EU, APAC regions. Customer data never leaves your boundary.
- Human-in-the-loop
- Configure approval gates to route edge cases to Slack, Teams, or email for review.
- Encryption at rest & in transit
- AES-256 at rest, TLS 1.3 in transit. BYOK supported for all customer-managed keys.
- Usage quotas & rate limits
- Per-team budgets and API quotas with real-time cost dashboards. No surprises.
Security built in, not bolted on.
Your data and your company brain never leave your boundary. Independently audited every year, with compliance controls enforced by the platform — not promised on a page.
Held and current — not roadmap targets.
SOC 2 Type IIOngoing security controls
GDPREU data protection
ISO 27001Global ISMS standard
HIPAABAA on enterprise plansTurn your onboarding and credit policies
into a competitive advantage.
Bring one policy document and one real onboarding or credit decision. We'll show you how Trail proposes rules and builds an agent, which then takes the correct decision and acts on it.
Prefer to read first? Read the FAQ →