Where UES.IO fits
Designed for Systems that Can’t Afford to Fail
Internal tools don’t make headlines. When they fail, everything slows down. When they’re built wrong, they become permanent problems. UES.IO is built for the applications your organizations will depend on for years — not the demos that get launched and forgotten.
Internal Tools + Operations
Replace fragile workarounds with systems you can trust.
Spreadsheets held together with macros. Approval processes in email threads. Operational data scattered across systems no one fully owns. These aren’t edge cases — they’re how most enterprise operations actually run.
UES.IO replaces these workarounds with durable internal applications: built quickly with AI-powered orchestration, secured at record level, extensible as requirements change, and governed for the long term.
Capabilities
- Multi-tenant applications deployed to any team instantly via managed packages
- Data access controlled by record, profiles and permission sets, not manual workarounds
- Full development lifecycle: build, test, deploy, rollback, repeat
- Human-approval checkpoints for agentic actions — your people stay in control
Real World Examples
REAL WORLD EXAMPLE:
Global Logistics Firm Replaces a Spreadsheet That Ran Its Business
The Problem:
A global freight company managed carrier rate negotiations through a single shared spreadsheet — 40+ tabs, dozens of embedded macros, and a web of email approvals layered on top. Over 200 people touched it monthly. When the analyst who built it left, no one fully understood the logic. Rates went out wrong, margins leaked, and a quarter-end audit flagged the process as a material control risk. Rebuilding it as “real software” had been quoted at 9 months and a seven-figure budget — so they kept patching the spreadsheet instead.
The Solution:
Using UES.IO, their internal team rebuilt the entire rate-management process as a durable internal application in under six weeks. AI-powered orchestration generated the data model, workflows, and UI from their existing logic. Access was secured at the record level — regional managers saw only their lanes, finance saw margins, carriers saw nothing they shouldn’t. Approval steps that once lived in email became governed checkpoints inside the app, with humans signing off on every rate change before it went live. As contract terms evolved, they extended the app instead of bolting on new macros — and deployed updates to every regional team instantly through managed packages.
The Savings:
The rebuilt process eliminated roughly 2,400 hours of manual work per quarter across the team and closed the audit finding entirely. Margin leakage from pricing errors — previously around $1.8M annually — effectively went to zero. Total measurable benefit in year one exceeded $3.5M, against a build cost a fraction of the original software quote, with ROI inside the first quarter.
This is the pattern we see repeatedly: the spreadsheet that “just works” is usually the one quietly putting the business at risk. We’ll rebuild one of yours in a half-day prototype session.ed for the long term
Application modernisation
Modernise without starting over.
Legacy systems represent real investment and institutional knowledge. A rip-and-replace approach wastes both. UES.IO connects to existing systems via OData and REST, allowing teams to modernise incrementally — building new interfaces, workflows, and agent-powered automation on top of existing data and processes.
Connecting legacy data cleanly also improves AI accuracy: agents get structured, reliable context rather than messy, fragmented inputs. Better data in means better decisions out.
Capabilities
- Connect to any proprietary database for restructuring
- Build new user experiences on existing data without migrating it
- Introduce AI-powered workflows without replacing foundational systems
- Clean context for AI agents — structured legacy data improves agent accuracy
- Connect legacy systems as-is. Modernize at your own pace.
Real World Examples
Case EXAMPLE:
Insurance Carrier Modernizes a 30-Year-Old Core System Without Touching It
The Problem:
A regional insurance carrier ran its policy administration on a mainframe-era system built over three decades. It worked — it held millions of policies and the institutional logic of the entire business — but it was slow to change, painful to integrate, and impossible to hire for. Underwriters jumped between green screens and side spreadsheets to do basic tasks. Every vendor pitched the same answer: a multi-year, eight-figure rip-and-replace that would risk the business and discard 30 years of embedded knowledge. Leadership kept saying no, and the modernization gap kept widening.
The Solution:
Using UES.IO, the carrier left the core system in place and built on top of it. UES.IO connected directly to the legacy database and existing services via OData and REST — no migration, no data moved. Their team built modern underwriter interfaces and streamlined workflows on the existing data, then layered in AI-powered automation for triage and document review. Because the legacy data was now exposed as structured, reliable context, the AI agents made noticeably more accurate decisions than earlier attempts fed by fragmented spreadsheet inputs. The core system kept running as the system of record; modernization happened incrementally, on the team’s own timeline, without a single high-risk cutover.
The Savings:
The carrier delivered a modern underwriting experience in months instead of years, avoiding an estimated $12M+ rip-and-replace program entirely. Underwriter handling time dropped by roughly 35%, and improved AI accuracy on triage reduced costly misclassifications. Net measurable benefit in the first year exceeded $4M, with the legacy investment preserved rather than written off — and zero disruption to live operations.
The lesson we see again and again: you don’t have to choose between risky replacement and standing still. You can connect to one of your legacy systems and build a working modern interface on top of it in no time.
AGENTIC WORKFLOW ORCHESTRATION
Orchestrate AI agents inside your governance model.
Building agentic applications on general-purpose AI tools means accepting their constraints: proprietary runtimes, opaque model dependencies, and governance gaps that enterprise security teams reject.
UES.IO is built from the ground up to host and orchestrate AI agents inside your security perimeter with record-level controls, audit logs, and the ability to switch models without rearchitecting.
Capabilities
- MCP-native agent orchestration with human-in-the-loop (HITL) support
- Agent control: profiles and permission sets built into the runtime and evaluated at user level
- Connect to external MCP servers, LLMs, and SLMs — or run private models on your own infrastructure
- Switch models freely — no rearchitecting, no lock-in, no downstream impact
- Policies and guardrails enforced at the platform level, and per-agent
- Agent traceability and cost control center keeps you within budget
Real World Examples
Case EXAMPLE:
Global Bank Deploys AI Agents Its Security Team Actually Approved
The Problem:
A multinational bank wanted to put AI agents to work across operations — processing requests, routing exceptions, drafting responses. But every general-purpose agentic platform they evaluated failed the same review: proprietary runtimes the security team couldn’t inspect, opaque dependencies on a single model vendor, and no way to enforce who an agent could act for or see. The risk and compliance teams rejected each one. Meanwhile, business units frustrated by the delay had started wiring agents to external AI tools on their own — creating exactly the ungoverned shadow-AI exposure the bank feared most.
The Solution:
Using UES.IO, the bank deployed agentic applications entirely inside its own security perimeter. Agents were orchestrated MCP-natively with human-in-the-loop checkpoints on every consequential action. Critically, agents inherited the same profiles and permission sets as users — evaluated at the platform level at runtime — so an agent could never see or do more than the person it acted for. The bank connected approved external MCP servers and ran sensitive models privately on its own infrastructure, switching between LLMs and smaller specialized models freely without rearchitecting. Every agent action was logged and traceable, with a cost-control center keeping spend within budget. Security signed off because governance wasn’t bolted on — it was the foundation.
The Savings:
The bank moved from stalled pilots to production agents in a single quarter, work that prior platforms had blocked for over a year. Eliminating shadow-AI usage closed a compliance exposure the bank valued in the tens of millions in potential regulatory and breach risk. Operational automation recovered an estimated $5–7M annually in manual processing, while model flexibility cut inference costs by roughly 30% versus single-vendor lock-in — all inside a governance model the security team owned end to end.
The pattern is consistent: the blocker to enterprise AI isn’t capability, it’s governance. We’ll stand up a governed agent on one of your real workflows in a half-day prototype session.
SECURE DATA WORKFLOWS
When data sovereignty and auditability aren’t optional.
For teams operating in regulated industries — financial services, healthcare, government, or any organisation subject to GDPR, DORA, or the EU AI Act — data governance is not a feature request. It’s a precondition for deployment with your preferred deployment configurations.
UES.IO treats data sovereignty as a design principle: record-level access controls, immutable audit logs, policies and guardrails built into the platform, and deployment in your own infrastructure so data never leaves your control.
Capabilities
- Banking-grade compliance: FINMA, GDPR, DORA, EU AI Act — built into the infrastructure of your choice
- Private cloud, on-premise, or air gapped deployment supported
- Proprietary data stays inside your perimeter — no third-party data residency risk
- Human-approval checkpoints for any agentic action affecting sensitive data
Real World Examples
Case EXAMPLE:
Wealth Management Firm Modernizes Its Legacy Quant Engine — and Turns It Into a Product
The Problem:
A wealth management firm had spent years building a sophisticated quant system — the analytical engine behind its portfolio models and risk calculations, and a genuine source of competitive edge. But it was locked in a legacy stack: hard to extend, impossible to surface on mobile, and unable to connect to modern AI tooling. The firm saw an opportunity beyond its own use — to package the engine and sell it on to banks — but couldn’t expose decades of proprietary quant logic without a multi-year rebuild, and couldn’t risk that intellectual property or its clients’ data leaving its control. Operating under FINMA, GDPR, DORA, and the EU AI Act, any solution also had to be deployable inside their own perimeter, full stop.
The Solution:
Using UES.IO, the firm modernized without rewriting the quant engine. UES.IO connected to the legacy system as-is via OData and REST — the proven calculation logic stayed exactly where it was, now exposed cleanly through MCP. On top of that foundation, the team built modern, mobile-facing apps for advisors and clients, and orchestrated AI agents that drew on structured quant outputs as reliable context rather than fragmented inputs. Crucially, the whole thing was packaged as a multi-tenant application deployable to each bank instantly through managed packages — with record-level access controls, immutable audit logs, and human-approval checkpoints on any agentic action. Each downstream bank ran it inside its own infrastructure, so neither the firm’s IP nor client data ever left a controlled perimeter, and compliance was built into the infrastructure rather than promised on top of it.
The Savings:
The firm turned a static internal asset into a new revenue line — productizing its quant engine for banks without the multi-year, eight-figure rebuild that exposing it conventionally would have demanded, avoiding an estimated $10M+ in development and certification cost. Mobile and AI-powered experiences reached advisors in months instead of years, and the managed-package model let each new bank be onboarded in days rather than through bespoke integrations. Most valuable was preserving the proprietary quant logic as durable IP — modernized, monetizable, and fully governed — rather than rebuilt and put at risk.
The pattern holds across every regulated client we work with: your hardest-won systems are assets to build on, not liabilities to replace. We’ll connect to one of your legacy systems and stand up a modern, governed app on top of it in a half-day prototype session.
Tell us what you’re building. We’ll tell you if UES.IO is the right fit.
We don’t need a lengthy requirements document. A short conversation about your environment, your constraints, and what you’ve tried before is usually enough.