Practical AI. Measurable outcomes.
We've built ML models that run in production, AI agents that talk to customers, and automation pipelines that quietly remove manual work. No 'AI transformation' deck. Three concrete offers below.
Award-winning AI2025 TechBehemoths Winner — Artificial IntelligencePick the one that matches your problem.
We do one of these three things, sometimes two together. Anything beyond that — drop us a line and we'll tell you honestly if we're the right team.
Custom AI agents
Chat, support, sales, internal tools. Reads context, takes an action, lives inside your stack — not as a bolt-on widget on someone else's domain.
Like ReachStack's outbound research bot — drafts a sequence in your voice, sends from your inbox, replies to easy threads.
Custom ML models
Classification, prediction, fine-tuning. Built on your data, deployed in your cloud. We don't do generic — we do the model that fits the problem.
Like Iris's transport-mode detection — 88% accuracy across two continents, MVP in 6 months, under 5% daily battery.
AI automation of workflows
RPA without the RPA tax. We replace the manual workflow with code, not a screen-scraper. The kind of thing that pays for itself in a quarter.
Common shapes: invoice triage, document classification, contract review, inbox auto-routing, multi-step research tasks.
Plus the boring things that make the above work.
Most AI projects fail at the integration layer, not the model layer. We do both.
LLM integration & RAG
OpenAI, Anthropic, open-weight. The right model for the price. Retrieval set up properly so answers stay grounded.
Fine-tuning
On your data, in your cloud. No "training on your prompts." We start cheap with prompts + RAG and tune only if metrics demand it.
Voice & multimodal
Speech-to-text, text-to-speech, vision, document understanding. We've shipped two voice agents in the past year.
On-prem & private
For when "send to OpenAI" isn't an option. Self-hosted Llama / Mistral / Mixtral in your VPC. Same agent UX, different inference layer.
Eval & monitoring
Test sets, accuracy tracking, drift detection. The unsexy half of an AI project that decides whether it stays in production.
Production deployment
AWS Bedrock, Modal, Replicate. Auto-scaling, cost monitoring, model versioning. The thing nobody shows on the demo.
Pilot, validate, scale.
Every AI engagement starts as a 2–3 week pilot with a kill-or-scale gate at the end. We don't sell six-month roadmaps on day one.
Pilot
Pick one workflow. Build a working agent. Measure baseline (current manual process) vs. it. Output: a working demo and a number.
Validate
Real users, real data, real edge cases. Hit the target accuracy / cost / latency — or kill the project. We'd rather lose the build fee than ship a bad agent.
Scale
Production deploy, evals, monitoring, retraining cadence. Most engagements continue on retainer because models drift and the world changes.
Model-agnostic by design.
We benchmark before we recommend. Latency, accuracy, cost — pick two, and the right model usually picks itself.
Where this service has shipped.
Two recent engagements that leaned heavily on this practice. Read the full case studies, or browse all work.
Custom ML model: 88% accuracy detecting how someone is traveling.
Sensor-fusion deep learning across gyroscope, accelerometer, GPS, magnetometer and barometer. Trained on multi-continent data. Inference on-device.
An AI agent that researches, writes and sends — in the rep's voice.
Multi-step research → tailored draft → multi-account send → inbox auto-reply. RAG over a public + internal knowledge base. Evals running continuously.
Related reading
AI Agents vs. Chatbots vs. Automation: What's the Difference?
Everyone's pitching 'AI' but the terms are used interchangeably. Understanding the real differences between workflow automation, chatbots and AI agents could save you from investing in the wrong solution.
ReadAgentic AI Explained: What Business Leaders Need to Know Now
Agentic AI isn't science fiction — it's being deployed right now. This guide explains what makes it different from chatbots and automation, which industries benefit most, and how to evaluate if it's right for your business.
ReadAI Glossary for Business Leaders: 28 Essential Terms
Cut through AI jargon. Learn the 28 essential AI terms every business leader needs to know — from LLMs and RAG to MLOps, guardrails and AI governance — with simple explanations and real-world examples.
ReadThe questions we get most.
Anything else? Email hello@ibute.tech — we reply within 24h.
Do you build chatbots?
What about training data?
Which LLM should we use?
Can you do voice agents?
Will the LLM hallucinate on our use case?
What's the difference between AI Solutions and Engineering at ibute?
Will my data train someone else's model?
Shipped across 10+ sectors.
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Have an AI solutions project in mind?
Free 30-minute review. We'll tell you whether this is the right fit, what the shape of the engagement would look like, and roughly what it costs. No deck. No follow-up unless you ask.
Austin · Pakistan · Reply within 24 hours.