05Service · AI Solutions

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.

Three offers

Pick 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.

Offer 0101 / 03

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.

Offer 0202 / 03

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.

Offer 0303 / 03

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.

Adjacent capabilities

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.

How we work

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.

01Week 1–3

Pilot

Pick one workflow. Build a working agent. Measure baseline (current manual process) vs. it. Output: a working demo and a number.

02Week 4–5

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.

03Week 6+

Scale

Production deploy, evals, monitoring, retraining cadence. Most engagements continue on retainer because models drift and the world changes.

Stack

Model-agnostic by design.

We benchmark before we recommend. Latency, accuracy, cost — pick two, and the right model usually picks itself.

LLMs
OpenAIAnthropicMistralLlamaGemini
Frameworks
LangChainLlamaIndexVercel AI SDKPydantic AI
ML
PyTorchTensorFlowHugging Facescikit-learn
Vector
pgvectorPineconeQdrantWeaviate
Deployment
AWS BedrockModalReplicateTogether AI
Eval
BraintrustLangSmithHeliconecustom eval sets
FAQ

The questions we get most.

Anything else? Email hello@ibute.tech — we reply within 24h.

Do you build chatbots?
Sometimes. More often we build agents — bots that take an action, not just answer. The line between the two is whether the bot ends up in your CRM, your ticketing system, your inbox — or just on a website widget.
Real answer: most of our production wins come from prompt engineering + RAG, not custom fine-tuning. We start there. We only fine-tune when evals show we genuinely can't hit the target without it.
It depends on your latency / accuracy / cost triangle. We benchmark against your real data before recommending. Sometimes the cheapest model wins; sometimes you genuinely need the frontier one.
Yes — Whisper or Deepgram for STT, GPT or Claude in the middle, ElevenLabs for TTS. We've shipped two production voice agents in the past year.
If the use case is 'free-form Q&A on a knowledge base,' yes — that's where RAG + careful prompt design matters. If it's 'classify this email into one of 14 categories,' essentially never. Pick the right tool for the job.
AI Solutions builds with ML/LLM at the core. Engineering builds traditional software. We pair the two for most real projects — an AI feature still needs a database, an auth layer, and a deploy pipeline.
Not if we set it up properly. OpenAI and Anthropic both offer zero-retention enterprise modes. Self-hosted open-weight models obviously don't share data at all. We pick based on your sensitivity.
Industries

Shipped across 10+ sectors.

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Get in touch

Have a 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.

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