Solutions
We find the workflows where AI can make a real difference, redesign them around the right mix of people, software and agents, and engineer the production system that makes it work. Then we measure the result against where you started.
How it runs
Map processes, systems, data, roles and pain points.
Start hereAI Opportunity Assessment: a fixed-scope review of your workflows that produces a prioritised opportunity map and roadmap
Score opportunities on business impact, feasibility, data readiness, risk, cost and time to value.
Decide what people, deterministic software, automation, AI assistants and agents should each do.
Start hereAI Workflow Pilot: one high-value workflow redesigned and built, with baseline and target measures agreed up front
Build one workflow end to end and measure it.
Extend what worked to the next workflows.
Start hereAI Transformation Programme: several workflows across process, data, integration, security, governance and training
Monitor, evaluate and improve in production.
Start hereManaged AI Operations: ongoing monitoring, evaluation, improvement and cost control for AI systems in production
AI Lens
Describe an operational challenge. Get a first look at where AI, automation or agents could help, and what to check before you start.
SYGNISYS AI Lens
Ready
Please don’t include confidential or personal information. Your description is processed by our AI provider to generate the findings.
A first look generated by AI from your description. It is not an assessment; our team can review it with you.
Many business platforms now include their own AI agents. We use them where they fit and build where they don’t.
Where a workflow lives mostly inside one platform, we configure what is already there.
Where it spans several systems or carries real risk, we build.
Either way, we integrate across your systems and test that the result is reliable and secure.
Data readiness comes first. Most AI initiatives stall on data access and quality, not on models. Every assessment checks data readiness, and every pilot includes the data engineering it needs.
Examples of the kind of workflow we redesign. They describe typical situations, not specific clients.
Illustrative use case · Insurance
Claims arrive as forms, photos, emails and reports. An AI workflow classifies each document, extracts the key fields and routes the claim, with adjusters reviewing anything outside clear rules.
Illustrative use case · Financial services
Analysts read long policy and transaction documents against changing regulatory requirements. An AI assistant highlights relevant clauses and gaps, and the analyst decides and records the outcome.
Illustrative use case · Finance operations
Invoices arrive in many formats. Extraction and matching run automatically against purchase orders, and a person approves before anything is paid.
Illustrative use case · Logistics
Dispatch teams chase late and failed deliveries across several systems. An agent gathers the shipment history, drafts the customer update and proposes a fix for the dispatcher to approve.
Illustrative use case · Any sector
Answers sit in shared drives, wikis and inboxes. A retrieval system answers staff questions with citations and respects each person’s existing access rights.
With the workflows where AI pays off. We map your processes, systems and data, then score opportunities on business impact, feasibility, data readiness, risk, cost and time to value. Most clients start with an opportunity assessment or a single workflow pilot.
We build. Our AI transformation work is implementation-led: we redesign the workflow, engineer the production system, then measure the result against where you started.
Baseline and target measures are agreed before a pilot starts, and the workflow is measured against its baseline once it runs in production.
Maybe not for everything. Where a workflow lives mostly inside one platform, we configure what is already there. Where it spans several systems or carries real risk, we build, and either way we integrate across your systems and test the result.
That is common: most AI initiatives stall on data access and quality, not on models. Every assessment checks data readiness, and every pilot includes the data engineering it needs.