Part of IOanyT’s engineering capability. Most clients reach this team through a Workflow Diagnostic or Proof Sprint. How we work
LLM app development to production: measured on your own cases
Your LLM or RAG feature works in the demo. With real users it gives different answers to the same question, costs more than planned, and nobody can say how often it is right. We take it to production against cases your experts have decided.
Proven with The Heartbeat, our own product live on AWS Marketplace: AI reviews every call, analysts verify every flag.
Our flagship product TheHeartbeat.ai is live on AWS Marketplace, reviewing every contact-centre call it receives, with analysts verifying every AI flag. We understand the full AI product journey because we've done it ourselves—from ideation to monetization.
Start here
LLM or RAG app to production
First we build a test set from cases your experts have decided, then we harden the app against it.
An evaluation set, run before every release
How often the answer is right against your experts, and how often it stays the same on a rerun.
Guardrails
Answers kept to a defined format, with fallbacks and a route to a person.
Retrieval fixes
Where the wrong documents come back, we fix what is retrieved and how.
Cost controls
Limits per request, caching and the right model size for each step.
Model-free steps moved to tested code
LLMs write and test the logic at build time, and deterministic code runs it.
Monitoring and handover
Monitoring, a release process and a handover session for your team.
How it works
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Step 1
Kick-off and test cases
We agree the feature in scope and collect real cases with the right answers, decided by your experts.
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Step 2
Evaluate
We run the cases and report how often the answer is right and how often it stays the same on a rerun, with a fix list.
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Step 3
Harden
Guardrails, retrieval fixes and cost controls, with the evaluation set run before every release.
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Step 4
Handover
Monitoring, a release process and a handover session. You own the code.
What you own
You own the code. No runtime or platform fee from us; your own cloud and model costs are shown per request. We have no commercial relationship with model providers.
What it does not cover
- Training or fine-tuning new models.
- Your model-provider and cloud bills.
- A compliance opinion or any certification of your system.
Not built yet, or not sure it should be? Start with a Proof Sprint: one workflow, built and measured on your own cases, for consistency and for accuracy against your own experts. You keep the code.
ISO/IEC 42001 certified for AI management, with ISO/IEC 27001 and ISO 9001.
We Built Our Own AI/ML SaaS Product from 0→1
TheHeartbeat.ai is our flagship AI-powered call analytics platform—proof of our end-to-end AI product development capabilities.
What TheHeartbeat Does:
AI-powered call analytics that analyzes 100% of contact center calls in real-time:
- Speech-to-text across Indian and international languages (mono/stereo)
- Sentiment and emotion analysis
- Call quality scoring and agent performance tracking
- Real-time alerts and batch analytics
- Integration with Google Drive, Dropbox, Box, GCP
Built on: AWS SageMaker, Lambda, S3, RDS | React dashboard | Speech and NLP models | Analysts verify every AI flag
💡 Why This Matters: TheHeartbeat proves we can build AI/ML products from concept to AWS Marketplace, handle real production data at scale, and deliver measurable business outcomes.
Our AI/ML Capabilities
Natural Language Processing (NLP)
- Speech-to-text & text-to-speech
- Sentiment & emotion analysis
- Named entity recognition
- Chatbots & conversational AI
Computer Vision
- Object detection & classification
- Facial recognition & analysis
- Image segmentation
- OCR & document processing
Predictive Analytics
- Time series forecasting
- Churn prediction
- Demand forecasting
- Recommendation engines
AWS AI Services
- SageMaker model training & deployment
- Rekognition & Textract
- Comprehend & Translate
- Bedrock (LLMs & Foundation Models)
End-to-End AI/ML Product Development
Discovery & Feasibility
Define use case, assess data availability, validate AI applicability, estimate ROI
Data Strategy & Preparation
Data pipeline design, labeling, cleaning, augmentation, versioning
Model Development
Experiment tracking, hyperparameter tuning, model selection, evaluation metrics
Production Deployment
Scalable inference, monitoring, A/B testing, CI/CD integration
Monitoring & Optimization
Performance tracking, model drift detection, retraining pipelines, continuous improvement
LLM app to production questions
Can you make it never hallucinate?
No one can promise that. We measure how often answers are right and consistent on your own cases, keep the model inside limits, and route the cases it shouldn’t decide to a person.
Which models and frameworks?
The provider and framework you already use. We have no commercial relationship with model providers, so we gain nothing from a switch.
Do we need a lot of labelled data?
No. We start with 30 to 100 cases your experts decide, and the set grows as the app does.
Can we start smaller?
Yes. Start with the evaluation and a fix list alone; build on it later with us or anyone else.
Start with the free Review.
Free, 60–90 minutes. Bring the system or decision you are weighing; we tell you what to check first, in a 2-page note you keep.
Fixed price, agreed at scoping. The quote comes from Pricient — by IOanyT, itemised with exclusions.
Prefer to start through Upwork, with escrow and milestone payments? Several of our fixed-scope engagements are available there. See them on Upwork