AI & Automation
Put AI to work on the tasks eating your team's week
Workflow automation, AI agents, and intelligent features built into the systems you already run — scoped against hours saved rather than technology deployed.
The Problem
What usually
goes wrong
Manual work that scales with headcount
Data entry, document processing, and routine replies grow in direct proportion to the business. Automation is what breaks that link.
AI pilots that never reach production
Demos are easy and production is not. Getting from an impressive prototype to something reliable enough to put in front of customers takes real engineering.
Data scattered across systems
An AI system is only as good as what it can see. Most of the work in any AI project is getting clean, current data into one place.
Not knowing where AI actually helps
Not every process should be automated. The return comes from picking the few that pay back quickly and leaving the rest alone.
What We Do
What's
included
Workflow Automation
Document processing, data entry, reporting, and hand-offs between systems automated end to end, with humans kept in the loop wherever judgment actually matters.
AI Agents & Chatbots
Support assistants, internal knowledge bots, and lead-qualification agents grounded in your own documents and data, so answers reflect your business rather than the open internet.
AI Features In Your Product
Search, recommendations, summarisation, classification, and generation built into your existing application through current model APIs.
Data Pipelines & Analytics
Consolidating data from across your tools into one reliable source, so your dashboards and your AI systems are both working from current numbers.
Process
How we
deliver
Opportunity Audit
We map where your team's hours actually go and score processes by time saved against effort to automate. You get a ranked shortlist, not a technology pitch.
Pilot One Workflow
We automate a single high-value process end to end and measure it against the manual baseline before anyone commits to expanding.
Production Build
The pilot gets hardened for real use: error handling, monitoring, human review steps, and access controls.
Rollout & Measurement
Wider deployment with your team trained on the escalation path, and ongoing tuning as both the models and your process change.
Tech Stack
What we
build with
We pick tools that are stable, well supported, and easy to hire for — so your project is never dependent on one person's favourite framework.
Python/Claude API/OpenAI API/LangChain/pgvector/Pinecone/n8n/Node.js/PostgreSQL/Docker
Industries
Who we
build for
Sectors where software carries real operational weight.
Why Zylience
How we
work
One team, start to finish
The people who scope your project are the ones who build it. Design, engineering, and launch happen under one roof — no subcontracting, no handoff to a team you never met.
You own everything
Source code, infrastructure, and accounts are in your name, with documentation at handover. Nothing about how we work locks you into staying with us.
AI-native by default
We build automation into what we deliver wherever it removes real work from your team — not because it belongs on a brochure.
Built for the version after this one
We architect for where the product is going, not just what ships this quarter, and we stay available after launch to keep it moving.
Engagement
Two ways to
work with us
Pricing follows scope, so we quote after discovery — not from a rate card that would be wrong for your project.
Model 01
Project-Based
Fixed scope, fixed price, fixed timeline.
Best when you know what needs building and want cost certainty. Covers discovery, design, development, testing, launch, and a post-launch support window.
- Quoted after discovery
- Milestone-based delivery
- Support window included
Model 02
Dedicated Team
A monthly retainer for continuous work on your product.
Best for evolving scope and long-term product development. You get a consistent team that knows your codebase, and you scale up or down with a month's notice.
- Monthly rolling
- Scale up or down
- Direct access to the team
AI & Automation FAQ
Questions,
answered
01Where should we start with AI?
With one process where the time cost is measurable and the output is checkable. Automating a single reporting or document workflow proves value in weeks and teaches you more than any strategy document.
02Is our data safe with AI models?
Enterprise API tiers from the major providers do not train on your data, and where the data cannot leave your environment we run open models in your own infrastructure. We agree the data boundary before building anything.
03Will AI replace our team?
In the projects we take on it removes the repetitive part of the job — the copying, sorting, and first-draft work — so the same people handle more volume. We design human review into anything with real consequences.
04How do you measure whether it worked?
We baseline the manual process first: time per task, error rate, and volume. After launch we measure the same things. If the numbers do not move, the automation is not working and we will say so.
05Can you add AI to software we already have?
Usually yes, through its API or database. We build alongside your existing system rather than asking you to replace it.
06What ongoing costs does AI have?
Model usage is billed per request, so running cost scales with volume. We estimate the monthly cost during the audit and design prompts, caching, and model selection to keep it predictable.
Next Step
Ready to start your ai & automation project?
Tell us what you're trying to build. We'll come back with scope, timeline, and a fixed quote — no obligation.