The intelligence layer
AI & LLM Integration for Product Teams
AI integration work is about putting a model into a real product with real users and real failure modes. Kiwimesh identifies where AI creates measurable value in your workflow, builds the data pipeline and evaluation harness under it, and ships a production feature in 4 to 12 weeks. We have shipped an AI emergency dispatcher for a healthcare platform and a voice assistant covering 250 metro stations.
“Everyone's talking about AI. We don't know where it fits for us — or how to build it.”
Not a chatbot bolted onto your homepage. We help you identify where AI genuinely creates value in your product or workflow, then build it — from data pipelines to production-ready models and integrations.
What this covers
- •AI opportunity mapping
- •LLM integration (GPT, Claude, LLaMA)
- •Speech-to-text and voice interfaces
- •Workflow automation with LLMs and agents
- •Data infrastructure and pipelines
- •Custom model training and fine-tuning
Best for
Product companies adding AI features, ops teams seeking automation, enterprises with unstructured data
Typical engagement
4–12 weeks for a first production feature
How it works
What happens, in order.
01
Opportunity mapping
A short workshop to find the two or three places where a model changes an outcome, and the places where it is a gimmick. We say no to the second group.
02
Data and evaluation first
Before building the feature we assemble the data it needs and a test set that defines what good looks like. Every later change is measured against it.
03
Build the feature
Model selection, prompt and tool design, retrieval where needed, guardrails, and the product surface. Cost and latency budgets are set up front.
04
Ship and monitor
Production deployment with logging of inputs, outputs, and user feedback so the feature improves after launch instead of degrading.
What you get
Deliverables you can hold us to.
- AI opportunity map with a ranked shortlist and what we recommend not building
- Evaluation dataset and harness with agreed quality thresholds
- Production feature with guardrails, fallbacks, and cost controls
- Data pipelines and retrieval infrastructure where needed
- Monitoring for quality, cost, and latency in production
- Handover documentation and prompt/tool versioning
Questions
What people ask before they book a call.
Which models do you work with?
Claude, GPT, Gemini, and open-weight models such as Llama, chosen per feature on quality, cost, latency, and data-residency requirements. We have also shipped speech-to-text with Whisper and custom fine-tuned models where the task needed it.
Can our data stay in our environment?
Yes. For regulated or sensitive data we deploy within your cloud account and use models with contractual no-training terms, or self-hosted open-weight models where residency requires it.
How do you keep the feature from making things up?
Retrieval over your own data, constrained outputs, tool use instead of free-text where facts matter, and an evaluation set that catches regressions before release. We also design the product surface so users can see and correct the model's work.
What does an AI feature cost to run?
We set a per-request cost budget during design and report against it. Most product features run at a fraction of a cent to a few cents per interaction; we tell you the number before you commit.
Related work
Where we have done this before.
Healthcare · Patient management
Multi-stakeholder healthcare platform
A six-app healthcare ecosystem — the product our client took to Shark Tank
Patient app, resident web, staff portal, admin dashboard, shared SDK, and an AI emergency dispatcher — all sharing a single backend with role-isolated access. Built for a healthcare startup that was later featured on Shark Tank with the product we delivered.
Read the case study
Transportation · Smart city
TransitPal
Multi-modal routing and cashless ticketing for public transit
Proprietary IP we built and productised — launched on the App Store and Play Store. 150+ APIs, dynamic fare engine, real-time vehicle tracking, ONDC-certified, and a voice AI for 250+ metro stations.
Read the case study
Tell us where you are. We'll tell you what it takes.
A 30-minute call, then a written scope and price within three business days.