Your team is answering the same questions and copying data between tools all day. We build AI systems that handle the repetitive stuff so your people can do work that actually requires a brain.
Hi, I need help with our customer support. We're getting 200+ tickets a day and drowning.
Got it. What percentage of those are repetitive questions? Shipping status, returns, order changes, that kind of thing.
Probably 70-80%. Same questions over and over. My team spends most of their day on it.
That's a strong automation candidate. I'm routing you to our AI team with a pre-qualified brief.
Brief sent to AI team. Estimated 140+ hrs/mo recoverable.
40+
AI systems deployed
200+
Hours saved per client/mo
60%
Avg task reduction
<4wk
Build time to production
Here's what happens to a support ticket in most businesses vs. what happens when AI picks it up.
4-6 hours average
Email arrives in shared inbox
Sits unread until someone checks
Someone reads and categorises it
Manually figured out, often mislabelled
Forwards to the right department
If they pick the right one. Sometimes bounces.
Waits in a queue
Hours pass. The customer follows up.
Someone writes a response
Typed from scratch, often repeating past answers
Under 3 minutes
Email arrives
Picked up instantly by the AI agent
AI reads, classifies, drafts response
Understands intent, checks knowledge base, writes a reply
Human reviews (if needed)
One click to approve. Most go straight through.
Response sent
Customer gets an answer in minutes, not hours
Different problems need different tools. Here's what we build most often.
Customer support bots, sales qualification agents, and internal knowledge assistants. They handle real queries with context, not scripted decision trees.
Beyond Zapier templates. Multi-step business processes with branching logic and error handling that don't break silently.
GPT-4, Claude, and Gemini APIs plugged into your product or internal tools. Prompt engineering, context management, and cost optimisation included.
Retrieval-augmented generation for document Q&A and knowledge base search. Your data surfaced accurately, with citations and confidence scores.
AI-assisted content creation: briefs, drafts, and SEO optimisation. Consistent output at scale without losing your brand voice.
Extract, classify, and summarise unstructured data at scale. Messy inputs go in, clean structured outputs come out, ready for your team to act on.
Every AI project is different. The tool you pick changes the economics and the timeline. Here's how we think about it.
AI Chatbot / Agent
Customer-facing or internal
Use this when you have a high volume of repetitive questions from customers or internal teams. The bot learns your business and your products, then handles the 80% that doesn't need a human.
Workflow Automation
Internal operations
Use this when your team is manually moving data between systems, sending follow-up emails, or updating spreadsheets. These are the workflows that eat 2-3 hours a day and nobody notices anymore because they're used to it.
RAG System
Document search and Q&A
Use this when you have a large body of documents (policies, product specs, manuals) and people waste time searching through them. RAG lets AI answer questions from your own data, with references to the source.
Custom LLM Integration
Product-level AI
AdvancedUse this when you want AI capabilities built into your product or a complex internal tool. We handle model selection, prompt chains, rate limiting, and cost optimisation so you don't burn through API credits.
847
Chats today
94%
Resolved
8s
Avg response
Enterprise clients are eligible for a full refund within 30 days of contract signing, or a pro-rated refund after 30 days based on usage. Refunds require VP-level approval for amounts over $10,000.
// API request to LLM gateway
POST /v1/completions
{
"model": "gpt-4-turbo",
"fallback": "claude-3-sonnet",
"max_tokens": 500,
"context_window": "auto"
}
200 OK
latency: 1.2s | tokens: 347 | cost: $0.004
model_used: gpt-4-turbo | cache_hit: true
A tight process that keeps scope under control and gets you something live fast.
We document your current workflows and identify which ones are worth automating based on time saved and error frequency. Not everything needs AI, and we'll tell you which is which.
Pick the right tool for each task: LLM, rule-based automation, no-code platform, or custom API. The choice changes the economics entirely, and we've built enough of these to know which one fits.
Working prototype in week one. Iterate with your team, full build in 3-6 weeks. You see progress early and can redirect before anything goes sideways.
Automated quality checks and human-in-the-loop where it matters. Automation that degrades silently is worse than no automation at all.
Businesses that automate now are locking in a cost advantage. The longer you wait, the more you're paying people to do what machines already do better.
77%
of companies using or exploring AI
Most of your competitors are already doing something with AI. The question is whether you're already behind.
McKinsey, 2024
40%
productivity gains from AI automation
Biggest gains come from killing repetitive data handling. Not the flashy stuff. The boring stuff.
BCG, 2024
80%
of queries handled without humans
Well-trained chatbots resolve routine support without escalation. The rest get better attention because your team isn't buried.
Gartner, 2024
3-6wk
from concept to production
You don't need a 6-month R&D project anymore. Prototype in week one, iterate, deploy within the month.
Percee Digital, 2024-25
We built Maya to handle our own lead qualification and client onboarding. It processes incoming enquiries, scores leads, routes them to the right team, and generates project briefs automatically.
A DTC e-commerce brand getting 300+ support tickets a day needed faster response times without hiring more agents. We deployed an AI chatbot trained on their product catalogue and shipping policies.
92%
Leads auto-qualified
35hrs
Saved per week
2min
Avg lead response
78%
Auto-resolved
45s
Avg response time
4.6
CSAT score
"We were sceptical about AI chatbots because every one we'd tried felt robotic. Percee built something that actually sounds like us. Our support team went from drowning to handling only the cases that genuinely need a human."
Rahul K.
Head of Operations, DTC Brand
"The workflow automation they built replaced a process that took our admin team 3 hours every morning. It runs in the background now and we barely think about it. Should have done this a year ago."
Sarah N.
COO, SaaS Company
"They set up a RAG system over our 500-page policy manual. New hires get answers in seconds instead of asking senior staff. It cites the source document every time, which was the big thing for us."
Tom A.
Director of Training, Healthcare Group
The biggest mistake is training on bad data. Here's what we learned from 40+ deployments about what separates a chatbot people use from one they ignore.
No-code tools are great until they aren't. How to tell when your workflows have outgrown Zapier and what to do about it.
Retrieval-augmented generation is how you make AI accurate with your own data. The architecture, the tradeoffs, and what to watch out for in production.
If yours isn't here, email us. We'd rather explain it than have you guess.
Ask us anything arrow_forwardAnything repetitive and rule-based is a candidate: answering common customer questions, classifying emails, extracting data from documents, generating reports, routing tickets. The rule of thumb is simple. If someone on your team does the same thing more than 20 times a week, it's probably automatable. We'll tell you if something isn't a good fit.
Most projects go from kickoff to production in 3-6 weeks. You'll see a working prototype in the first week. Simple workflow automations can be live in under 2 weeks. Complex RAG systems or custom LLM integrations take closer to 6-8 weeks. We scope tightly and build incrementally, so you're never waiting months with nothing to show for it.
Your data stays yours. We use enterprise API tiers from OpenAI and Anthropic where your data is not used for model training. For sensitive industries like healthcare and finance, we can deploy on your own infrastructure or use Azure/GCP private endpoints. Data masking and PII filtering are standard. Every project includes a data handling agreement.
We're model-agnostic. GPT-4 and GPT-4o handle most general-purpose tasks, Claude is better for long-document processing, and Gemini covers multimodal work. For cost-sensitive applications we use smaller models like GPT-4o-mini or Claude Haiku. We often run multi-model setups: hard tasks go to the bigger model, routine ones use something cheaper and faster.
Yes. AI systems degrade over time as your business changes, so they need monitoring. We offer maintenance packages covering performance monitoring, model updates, and prompt refinement based on real usage data. You can also bring it in-house if you prefer. We document everything and do a proper handover.
That's the whole point. We connect to whatever you're already using: Salesforce, HubSpot, Zendesk, Slack, Shopify, custom APIs, databases. If it has an API, we can integrate. We build on top of what you have.
It's a real concern. For customer-facing applications, we use RAG to ground responses in your actual data, not the model's general knowledge. We add confidence scoring so the system knows when it's unsure and escalates to a human. We also build in guardrails: topic boundaries and response validation. Nothing goes live without testing against real scenarios from your business.
Size matters less than repetition. A 10-person company where 3 people spend half their day on the same tasks has a stronger case than a 500-person company where everyone does unique work. If your team does the same thing repeatedly and it follows a pattern, automation saves time regardless of headcount. We've built systems for teams of 5 and teams of 500.
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Read more arrow_forwardSend us the task your team spends the most time on. We'll show you exactly what automation would look like, with a clear picture of what's realistic and what isn't.