Artificial intelligence
Put AI to work inside the tools your teams already use.
We design, build and integrate AI features into your existing products and workflows: assistants, document processing, search and prediction, from proof of concept to production.
What we deliver
Practical AI, connected to your data and systems.
01
AI assistants
Assistants that answer from your own documents and data, inside the channels your teams use.
02
Document processing
Extract, classify and route invoices, claims and contracts automatically.
03
Search & knowledge
Retrieval across internal knowledge, with sources your teams can verify.
04
Predictive analytics
Forecast demand, churn and risk from the data you already collect.
05
Workflow automation
Connect AI to ERP, CRM and back-office systems to remove manual steps.
06
AI for retail
Recommendations, smart checkout and stock prediction for e-commerce and stores.
Problems we solve
Sound familiar?
Most companies have tried a chatbot. Few have an AI feature that saves hours every day, inside the tools their teams already use, with their data kept under control. That is what we build.
01
“Our team spends hours reading, sorting and retyping documents.”
Invoices, claims, contracts, CVs, support tickets. An AI pipeline extracts the fields, classifies the document and routes it to the right person or system, with a human check where it matters.
02
“Knowledge is scattered across drives, wikis and inboxes.”
An assistant that answers from your own documents, with the source shown next to every answer, so people trust it and stop asking the same colleague.
03
“We tried an AI pilot and it never reached production.”
We design for production from day one: access control, monitoring, cost limits, evaluation sets and a rollback plan.
04
“We cannot send our data to a public model.”
Private deployments, regional hosting, data minimisation and a clear record of what is sent where. GDPR, Singapore PDPA and Vietnam’s PDPL are part of the design, not an afterthought.
How we deliver AI
Start small, prove value, then scale.
01
Identify
We rank use cases by business value, feasibility and data readiness.
02
Prototype
A working proof of concept on your own data, tested with real users.
03
Integrate
A production build connected to your systems, with monitoring and access control.
04
Improve
We measure accuracy and usage, then refine and extend.
What you get
Concrete deliverables, not a slide deck.
A first AI feature in production within a quarter, with the numbers to decide what comes next.
01
Use-case assessment
Your candidate use cases ranked by value, feasibility and data readiness, with a recommended first project.
02
Working proof of concept
Built on your data and tested with your users in two to six weeks, with measured accuracy.
03
Production integration
Connected to your ERP, CRM, help desk or intranet, with monitoring, logging and access control.
04
Evaluation and improvement loop
A test set, quality metrics and a monthly review so the system keeps improving after launch.
Why Synova
Model-neutral
Claude, OpenAI, Gemini, open-weight models or your own: we pick per use case on quality, cost and data constraints.
Integration first
AI is useful when it sits inside the workflow. Our teams have integrated ERP, CRM and back-office systems since 2010.
Measured, not promised
Every project ships with an evaluation set and a dashboard. You see accuracy, usage and cost.
Engineers, not slides
AI engineers, data engineers and QA in Ho Chi Minh City, led from Singapore and Europe.
Questions we hear
Frequently asked questions
Something else on your mind? Contact us and a consultant will answer within one business day.
Which AI use case should we start with?
The one with a clear repetitive task, available data and a measurable outcome: document processing, internal search or first-line support are typical first projects.
How do you keep our data private?
We minimise what is sent to a model, use private or regional endpoints where required, log every call and agree a data map with you before any build. Open-weight models can run inside your own cloud.
What does an AI proof of concept cost?
It depends on the data and integrations involved. Most proofs of concept fit the 10k–50k USD band on our quotation form and take two to six weeks.
Can AI work with our old ERP?
Usually yes. We connect through its database, APIs or exports, and we have integrated systems far older than most current ERPs.
Free consultation
Where could AI save your team the most time?
Tell us about your project. We'll propose the right approach, team and budget.