Data Semantics designs, builds and runs AI agents that investigate issues, resolve them within your guardrails, talk to customers and suppliers by email, chat and voice, and post the result into your ERP and CRM. People handle only the exceptions.
Work arrives
Invoices & ordersCustomer emails & callsSupplier queriesERP & CRM exceptionsBank statementsAI agents
Ontology & context graph
Acts in
SAPOracleDynamics 365SalesforceData Semantics builds AI agents that investigate exceptions, resolve them within your guardrails, talk to customers and suppliers, and post the result into your ERP and CRM. We start from ready-made products, Serina for accounts payable and Zebo for order-to-cash, and build custom agents on Azure OpenAI, Copilot Studio, Microsoft Agent Framework or open-weight models.
Copilots and chatbots help a person who asks. Exception-heavy work, such as invoices that don’t match, customers who haven’t paid and suppliers waiting for answers, still sits in inboxes until someone has time. AI agents take the case, work it through your systems within your rules and hand people only what needs judgement.
Each agent works a case from start to finish, the way a capable team member would, and writes the result back into your systems.
AI agents use analytics to monitor outcomes, spot patterns and improve the next decision.
For the most common processes we have products already in production. For everything else, we build custom agents on the same foundations.
AI agents for accounts payable: capture, match, resolve exceptions with suppliers and post to the ERP, end to end. 167 hours down to 2 per 1,000 invoices.
Visit serina.aiAI agents from first inquiry to cash collected: bookings and orders, service requests, collections and conversations by chat, email and voice.
Explore ZeboAgents designed for your own processes, such as procurement, reconciliation or operational exceptions, built on Microsoft and leading AI models.
See use casesHigh volume, many exceptions, clear rules and a measurable outcome. That's where an agent beats another dashboard or another hire.
Agents capture invoices, match them to orders and receipts, chase suppliers about mismatches and post clean invoices to the ERP.
Agents score accounts, call and message customers, log promises to pay, escalate disputes and reconcile payments.
Agents answer routine inquiries by chat, email and voice, check the customer's records first and hand complex cases to a person.
Agents handle purchase requests, supplier onboarding checks and supplier follow-ups that usually sit in inboxes.
Agents match transactions across bank, ERP and sub-ledgers, investigate breaks and prepare close schedules for review.
Agents watch for stock-outs, delayed orders, service-level breaches or handover snags, and work them to closure.
Tell us the process and the systems it runs on. We’ll show you what an agent would handle and what stays with your team.
An agent is only as safe as what it understands. Ours run on your ontology, so they know what your data means, and your context graph, so they know your policies, limits and who to ask. That's why clients let them act.
Figures from client deployments. Your own baseline and targets are set together before go-live.
AP processing effort per 1,000 invoices
Serinacollectors needed after agents took over the workflow
Zebocontact-center traffic
100,000 conversations a monthinvoices processed by our agents in the last year
Across 200+ legal entitiesWe start where the value is measurable, the pain is real and the agent can be governed.
Score your processes for volume, exceptions, rules and value, and pick the first agent.
Define what the agent may do, its limits, approvals, tone and when it hands back to a person.
Connect it to your systems and channels, and test it against real past cases before go-live.
Go live against a baseline. Our AI Agent Operations team monitors, tunes and reports.
No multi-year AI program. One agent in production, measured, then the next.
We score your processes, design the guardrails for the best candidate and put one agent live on real cases.
We run your agents in production and keep improving them, so they stay accurate, safe and cost-effective.
About agentic AI
RPA repeats fixed clicks and keystrokes and breaks when a screen or document changes. An AI agent reads documents and messages, checks records, applies your rules to cases it has not seen before and asks a person when unsure. Data Semantics uses RPA only for legacy screens with no API, and AI agents where work needs reading, checking or judgement.
Where agents fit
AI agents take over the repetitive, high-volume part of a process so your people handle exceptions, relationships and decisions. In Data Semantics deployments, teams typically absorb growth without adding headcount and move experienced staff to higher-value work.
Safety and control
Every agent works within guardrails: the policies, limits and approval thresholds you set, held in a context graph. It acts only through approved system connections, escalates anything outside its limits, and logs every step with its reasons. Agents are tested against real cases before release.
Yes. Every Data Semantics agent logs each step, the records it used, the rule it applied and any approval, so finance, risk and audit teams can trace any action from start to finish.
Platforms and deployment
Data Semantics builds on Azure AI Foundry, Microsoft Copilot Studio, the Microsoft Agent Framework, Azure OpenAI, Power Platform and Logic Apps, and uses models from OpenAI, Anthropic’s Claude and open-weight families such as Llama, Mistral, Qwen and DeepSeek where they fit best. Agents connect to SAP, Oracle, Dynamics 365, Salesforce and other systems through their APIs. Serina and Zebo also expose MCP servers, tested with Microsoft Copilot, Claude and ChatGPT, so people can ask about invoices, orders or collections and have the agents act from the assistant they already use.
Yes. Agents run in your own Azure tenant and preferred region by default. For banks and other regulated organizations, private and on-premises deployments are available on engagement.
Getting started
With Data Semantics, a fixed-scope readiness assessment and pilot puts one agent live on real cases in weeks, not months. Serina typically goes live in 4 to 6 weeks and Zebo in 6 to 8 weeks, with individual Zebo modules faster, unless customization or other dependencies apply.
Data Semantics prices the Agent Readiness & Pilot as a fixed-scope, fixed-price engagement. Serina is priced by invoice volume and Zebo by transaction volume, and AI Agent Operations is a monthly managed service.
Data Semantics can, through our AI Agent Operations service: monitoring accuracy and outcomes, updating guardrails and prompts, managing model costs and keeping the audit trail, under agreed service levels.
Against a baseline taken before go-live, using the outcome the process exists for: cost per invoice, days to collect, resolution time, calls handled, cash collected or hours saved.
Bring us the process your team spends the most time chasing. We'll show you what it looks like run by agents, on your own data, with you in control.
