# Prodinit — AI Engineering Partner
# https://prodinit.com

Prodinit builds AI products that work in production — custom LLM pipelines, voice AI, LLMOps, and model finetuning. 15+ AI products shipped. Book a call.

## What We Do

We design and build AI solutions that save time, reduce costs, and create smarter user experiences tailored for startups and enterprises.

## Services

### Custom AI Development
We design and build end-to-end AI systems tailored to your workflows from LLM pipelines and voice AI to automation tools.
URL: https://prodinit.com/services/custom-ai-development

### AI Strategy and Consulting
We identify where AI actually creates leverage in your business, define the right architecture, and give you a clear execution roadmap — no vague recommendations.
URL: https://prodinit.com/services/ai-strategy-consulting

### AI Infrastructure & LLMOps
We set up the backbone for reliable AI systems — deployment pipelines, monitoring, evals, scaling, and cost control — so your models run smoothly in production.
URL: https://prodinit.com/services/ai-infrastructure-llmops

### Model Finetuning & Optimization
We improve model performance for your specific use case through fine-tuning, prompt engineering, RAG optimization, and evaluation frameworks.
URL: https://prodinit.com/services/model-finetuning-optimisation

## Key Stats

- 5+ Years in production AI
- 15+ AI products shipped
- 100% Client retention
- 3 Countries served

## Case Studies

### LiveKit Voice AI Microservice and ECS Modernization for a Sales Simulation SaaS (Cuebo)
10x scale, zero concurrent session limits
URL: https://prodinit.com/case-studies/cuebo-livekit-voice-ai

### LLM-Powered BI Dashboard for Clinical Trial Oversight in Mental Health Research
Real-time clinical trial insights for all stakeholders
URL: https://prodinit.com/case-studies/llm-bi-dashboard-clinical-trials

### GPT-4.1 to GPT-4o-mini Distillation Pipeline for a High-Volume Voice AI Platform
70% AI inference cost reduction at 10k calls/day
URL: https://prodinit.com/case-studies/llm-model-distillation-voice-ai

### Air-Gapped EKS Deployment for a Regulated Fintech Platform
SaaS deployment in AWS EKS in 4 weeks
URL: https://prodinit.com/case-studies/air-gapped-eks-fintech

## Answers (AI Engineering Glossary)

### AI Engineering Partner
An AI engineering partner is a specialist team that designs, builds, and ships production AI systems alongside your company — owning the engineering, not just advising on strategy. Unlike a consultancy that delivers recommendations or a freelancer who completes a single task, a partner takes a product from prototype to reliable, scaled production.
URL: https://prodinit.com/answers/what-is-an-ai-engineering-partner

### Air-Gapped AI
Air-gapped AI is the practice of running AI models — including large language models — on infrastructure with no inbound or outbound internet connection. Data, model weights, and inference all stay inside a private network or isolated cloud environment, so sensitive information never crosses the organisation's security boundary.
URL: https://prodinit.com/answers/what-is-air-gapped-ai

### Canary and Shadow Deployments
Canary and shadow deployments are two safe ways to roll out a new model or prompt. A canary sends a small slice of real traffic to the new version and grows it only if quality holds. A shadow deployment sends traffic to the new version in parallel without showing users its output, so you can compare before any risk.
URL: https://prodinit.com/answers/what-are-canary-and-shadow-deployments

### Checkpoint and Resume
Checkpoint and resume is a pattern that lets a long-running AI agent save its state at safe points and continue from there after an interruption — a crash, a timeout, or a pause for human input. Instead of restarting from scratch and repeating expensive work, the agent reloads its last checkpoint and proceeds.
URL: https://prodinit.com/answers/what-is-checkpoint-and-resume

### File System as Context
File system as context is a pattern where an AI agent uses files on disk — not the prompt — as its working memory. Instead of holding everything in a limited context window, the agent reads and writes files, then loads only what each step needs. This lets agents work over far more information than a context window can hold.
URL: https://prodinit.com/answers/what-is-file-system-as-context

### Fine-Tuning
Fine-tuning is the process of further training a pre-trained large language model on a smaller, task-specific dataset so it adapts to a particular style, domain, or behaviour. It adjusts the model's weights — unlike prompting or RAG — making the new behaviour intrinsic to the model rather than supplied at query time.
URL: https://prodinit.com/answers/what-is-fine-tuning

### LLM Cost Attribution
Cost attribution for LLM applications is the practice of tracing token spend back to the thing that caused it — a feature, customer, request type, or agent step. Instead of one opaque monthly bill, you get a per-unit breakdown that shows where money goes, which is the prerequisite for controlling and optimising LLM cost.
URL: https://prodinit.com/answers/what-is-llm-cost-attribution

### LLM Evaluation
LLM evaluation is the practice of measuring the quality of a large language model's outputs against defined criteria — accuracy, faithfulness, tone, and safety — rather than assuming they are correct. It uses scoring rubrics, golden datasets, LLM-as-judge methods, and human review to make a non-deterministic system measurable and safe to ship.
URL: https://prodinit.com/answers/what-is-llm-evaluation

### LLM-as-Judge
LLM-as-judge is an evaluation method where a capable language model scores another model's outputs against a rubric, instead of relying on human review for every case. It lets teams evaluate thousands of responses for correctness, faithfulness, and tone at a scale humans can't match — and is validated against human judgments to confirm the judge is reliable.
URL: https://prodinit.com/answers/what-is-llm-as-judge

### LLMOps
LLMOps (Large Language Model Operations) is the set of practices, tools, and infrastructure for deploying, monitoring, evaluating, and continuously improving large language models in production. It extends MLOps with concerns specific to LLMs — prompt management, output evaluation, hallucination detection, token-cost control, and observability over non-deterministic responses.
URL: https://prodinit.com/answers/what-is-llmops

### Mixture of Agents
Mixture of Agents (MoA) is a pattern where several agents independently produce candidate answers to the same task, and an aggregator agent synthesises them into a single, stronger response. By combining diverse attempts — often from different models or prompts — MoA improves quality and robustness over any one agent acting alone.
URL: https://prodinit.com/answers/what-is-mixture-of-agents

### Model Distillation
Model distillation is a technique for transferring the knowledge of a large, capable 'teacher' model into a smaller, cheaper 'student' model. The student is trained to reproduce the teacher's outputs, so it can deliver comparable quality on a target task at a fraction of the inference cost and latency.
URL: https://prodinit.com/answers/what-is-model-distillation

### Multi-Agent Deadlocks
A deadlock in a multi-agent system occurs when two or more agents are each waiting on the other to act, so none can proceed and the system stalls. It typically arises from circular dependencies, agents waiting on shared resources, or coordination loops where every agent expects another to move first.
URL: https://prodinit.com/answers/what-causes-deadlocks-in-multi-agent-systems

### Orchestrator-Specialist Pattern
The orchestrator-specialist pattern is a multi-agent design where one orchestrator agent plans and delegates work to a set of narrow specialist agents, then assembles their results. The orchestrator owns control flow and state; each specialist does one job well. It keeps large agent systems debuggable by separating coordination from execution.
URL: https://prodinit.com/answers/what-is-orchestrator-specialist-pattern

### Parallel Tool Calls and Partial Failures
Parallel tool calls are when an AI agent invokes several tools at once instead of one at a time, cutting latency when the calls are independent. A partial failure is when some of those parallel calls succeed and others fail — and handling it well means the agent reasons over what came back rather than crashing or hallucinating the missing results.
URL: https://prodinit.com/answers/what-is-parallel-tool-calls

### Prompt Caching
Prompt caching is a technique that stores the processed form of a repeated prompt prefix so the model doesn't reprocess it on every call. When many requests share a large, stable prefix — a system prompt, instructions, or retrieved context — caching it cuts both cost and latency, since the model only processes the new part of each request.
URL: https://prodinit.com/answers/what-is-prompt-caching

### Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique that improves large language model responses by retrieving relevant information from an external knowledge source — documents, a database, or a vector store — and supplying it to the model at query time. This grounds answers in current, specific data the model was never trained on.
URL: https://prodinit.com/answers/what-is-rag

### Streaming and Partial Results
Streaming is when an LLM application sends its response token by token as it's generated, instead of waiting for the full answer. Those incremental tokens are partial results. Streaming cuts perceived latency dramatically — the user sees or hears output almost immediately — which is essential for chat and non-negotiable for real-time voice AI.
URL: https://prodinit.com/answers/what-is-streaming-partial-results

### Tool Schema Design
Tool schema design is the practice of defining the tools an AI agent can call — their names, parameters, types, and descriptions — so the model reliably picks the right tool and supplies valid arguments. A good schema is the interface between the model's reasoning and your code; its clarity largely determines whether tool use succeeds.
URL: https://prodinit.com/answers/what-is-tool-schema-design

### Voice AI Agent
A voice AI agent is a software system that holds a real-time spoken conversation with a user. It chains speech-to-text, a large language model, and text-to-speech into a low-latency loop, so a caller can speak naturally and hear a generated response — handling tasks like support, sales, or scheduling entirely by voice.
URL: https://prodinit.com/answers/what-is-a-voice-ai-agent


## Blog

### How to Detect LLM Hallucinations Before Users Do
A practitioner's guide to LLM hallucination detection in production — covering self-consistency sampling, reference-based scoring, RAG groundedness checks, and the quality-gate pattern that stopped regressions in a 10K calls/day voice AI system.
URL: https://prodinit.com/blog/llm-hallucination-detection-production

### LLM Observability in Production: What to Log and Why
A practitioner's guide to LLM observability in production — the four things to log per call, Langfuse instrumentation patterns, custom quality score tracking, and the alerting layer that catches regressions before users report them.
URL: https://prodinit.com/blog/llm-observability-production

### Private LLMs for Regulated Industries: A Buyer's Guide
A practitioner's buyer's guide to private LLMs for regulated industries — covering the two deployment patterns, what compliance actually requires at the infrastructure layer, and a procurement checklist from a team that shipped air-gapped LLM infrastructure in 4 weeks.
URL: https://prodinit.com/blog/private-llm-regulated-industries

### Air-Gapped AI for Fintech and Regulated Finance
Prodinit builds air-gapped AI infrastructure for regulated fintech — private EKS clusters with zero internet egress, Bedrock via VPC endpoint, and compliant CI/CD — from a team that delivered a full production air-gapped stack in 4 weeks.
URL: https://prodinit.com/blog/air-gapped-ai-fintech

### AI Strategy Consulting for Startups: What It Covers and When to Hire
Prodinit delivers AI strategy consulting for startups: use-case prioritisation, build-vs-buy analysis, LLM stack selection, and a sequenced 90-day roadmap — so your engineering team executes the right thing the first time.
URL: https://prodinit.com/blog/ai-strategy-consulting-startups

### Healthcare AI Development Partner
Prodinit builds production healthcare AI: HIPAA-compliant LLM infrastructure, clinical trial analytics, patient-facing voice agents, and real-time data layers for digital health companies.
URL: https://prodinit.com/blog/healthcare-ai-development-partner

### Testing Voice Agents: Barge-In, Latency and Structured Eval Logs
A practical how-to on testing voice agents for barge-in detection, latency segments, and structured eval logging — including test scenarios, threshold targets, and the eval harness Prodinit runs in production.
URL: https://prodinit.com/blog/testing-voice-agents-barge-in-latency

### Model Distillation for LLMs: Cut Inference Cost Without Losing Quality
A production playbook for LLM model distillation — from teacher-student dataset generation to fine-tuning and eval gates, with a GPT-4.1 to GPT-4o-mini pipeline as the proof.
URL: https://prodinit.com/blog/model-distillation-guide-llms

### Self-Hosted LiveKit vs LiveKit Cloud: Cost and Scale Trade-offs
Self-hosted LiveKit vs LiveKit Cloud: when to migrate, what you give up, what you gain, and what operating the self-hosted stack actually requires — from running both in production.
URL: https://prodinit.com/blog/self-hosted-livekit-vs-livekit-cloud

### On-Prem LLM Deployment: Ollama, vLLM and NVIDIA NIM in Production
A production guide to on-prem LLM deployment — how Ollama, vLLM, and NVIDIA NIM compare on throughput, GPU sizing, and operational maturity, with a decision framework for choosing a serving runtime.
URL: https://prodinit.com/blog/on-prem-llm-deployment

### LiveKit vs Pipecat for Production Voice AI: A Practitioner's Comparison
A practitioner's comparison of LiveKit vs Pipecat for production voice AI — transport, pipeline flexibility, scaling model, telephony, and observability, from running both in production.
URL: https://prodinit.com/blog/livekit-vs-pipecat-production-voice-ai

### HIPAA-Compliant LLM Deployment: Architecture for Healthcare AI
Architecture patterns for HIPAA-compliant LLM deployment — BAA coverage, PHI de-identification with Microsoft Presidio, VPC-private inference, and audit logging from production healthcare AI.
URL: https://prodinit.com/blog/hipaa-compliant-llm-deployment

### LLMOps Consulting Services: What They Cover and When to Hire
A BOFU guide to LLMOps consulting services — what they cover, when to hire, how consulting compares to in-house, and what an 8–12 week engagement delivers in practice.
URL: https://prodinit.com/blog/llmops-consulting-services

### How to Evaluate Voice AI Agents: Metrics Framework and Tooling
Five-layer voice AI evaluation framework: latency by stage, WER, barge-in handling, response quality, and call outcome rate — with Langfuse instrumentation and CI testing patterns.
URL: https://prodinit.com/blog/voice-ai-evaluation-framework

### LLM Evaluation Rubric: A Production Scoring Template
A practitioner's guide to designing LLM evaluation rubrics that hold up in production — five scoring dimensions, a ready-to-use judge prompt template, calibration steps, and CI gate thresholds.
URL: https://prodinit.com/blog/llm-evaluation-rubric

### How to Give an AI Voice Agent a Phone Number with Cloudonix
How to connect VAPI, Retell, and ElevenLabs voice agents to the public phone network with Cloudonix SIP trunking — the cx-vcc CLI, inbound routing, outbound BYOC, and passing data via SIP headers.
URL: https://prodinit.com/blog/ai-voice-agent-phone-number-cloudonix

### Self-Hosting LiveKit at Scale: Architecture from 90K+ Calls/Month
The complete production architecture for self-hosting LiveKit — standalone server, Python agent workers, LiveKit Egress on ECS, and multi-metric autoscaling from a team running 90K+ calls/month with five selectable AI pipelines.
URL: https://prodinit.com/blog/self-hosted-livekit-production-guide

### Cloudonix Core Concepts: CXML, Sessions, and Building Voice Agents
What CXML, sessions, the Converse verb, and Cloudonix's SDKs actually are — and how the pieces fit together to build a production voice agent on the Cloudonix platform.
URL: https://prodinit.com/blog/cloudonix-cxml-build-voice-agents

### Air-Gapped LLM Deployment: Run Private Models with Zero Egress
A practitioner's guide to air-gapped LLM deployment — the two architectures that work (self-hosted open-weight models vs Bedrock via VPC endpoint), GPU sizing, model ingestion, and the security controls regulated buyers require.
URL: https://prodinit.com/blog/air-gapped-llm-deployment

### Connect LiveKit to the Phone Network with Cloudonix SIP Trunking
A practitioner's guide to connecting a self-hosted LiveKit voice stack to the public phone network with Cloudonix SIP trunking — SIP URI registration, CXML inbound routing, outbound BYOC, and the SBC work you skip.
URL: https://prodinit.com/blog/livekit-cloudonix-sip-trunking

### AI Engineering Consulting Startups: What They Are and When to Choose One
A definition guide for CTOs evaluating AI engineering consulting startups versus large agencies — covering what they build, how they differ, when to choose one, and what to look for before signing.
URL: https://prodinit.com/blog/ai-engineering-consulting-startup

### RAG Pipeline Chunking Strategies: Split Documents for Better Retrieval
A practitioner's guide to RAG pipeline chunking strategies — covering fixed-size, semantic, structural, and hierarchical approaches with chunk size guidance and a decision matrix for each corpus type.
URL: https://prodinit.com/blog/rag-pipeline-chunking-strategies

### How to Hire AI Engineers in 2026 (Build vs Partner)
A decision framework for CTOs and engineering leads evaluating whether to hire AI engineers in-house or partner with an AI engineering firm — covering salary benchmarks, hiring timelines, delivery speed, and when each path wins.
URL: https://prodinit.com/blog/hire-ai-engineers-2026

### LLMOps in 2026: AI Demo to Production Guide
A 2026 LLMOps guide for teams stuck at the demo stage — the six-layer production stack (serving, evals, observability, CI/CD, cost control, governance), a phased rollout, and the mistakes that keep AI systems out of production.
URL: https://prodinit.com/blog/llmops-guide-2026

### How to Add AI to Your SaaS Product Without Hiring a Machine Learning Team
A practical guide for SaaS founders and CTOs on adding AI features to an existing product — covering the 4-phase framework, RAG vs agents vs fine-tuning decision guide, minimum viable LLMOps, and build vs consult trade-offs.
URL: https://prodinit.com/blog/add-ai-to-saas-product

### Questions to Ask an AI Consulting Firm Before You Sign: A CTO's 8-Point Checklist
A practical buyer guide for CTOs and engineering leaders evaluating AI consulting firms — eight questions that separate credible AI engineering partners from over-promising vendors, with red flags and green flags for each.
URL: https://prodinit.com/blog/questions-to-ask-ai-consulting-firm

### AI Agents in Production: 7 Architecture Mistakes That Sink Your System
The 7 most destructive AI agent architecture mistakes in production: god agents, stateless memory, missing tool-call guardrails, no observability, absent eval loops, unbounded cost spirals, and no human escalation path — with before/after fixes for each.
URL: https://prodinit.com/blog/ai-agents-production-mistakes

### LLM Fine-Tuning vs RAG: A Production Decision Framework for Engineering Teams
A practical decision framework for engineering teams choosing between RAG and LLM fine-tuning in production — with real cost comparisons, a decision flowchart, and a guide to LoRA, QLoRA, SFT, and DPO.
URL: https://prodinit.com/blog/llm-fine-tuning-vs-rag-decision-framework

### How to Build a Clinical Trial Dashboard Teams Actually Use
A practitioner's guide to clinical trial dashboards — the four metric families to track, real-time vs batch reporting, natural-language-to-SQL access, and when a custom build beats off-the-shelf CTMS reporting.
URL: https://prodinit.com/blog/clinical-trial-dashboard

### LLM Cost Optimization: Cut AI Inference Costs 47–80% Without Sacrificing Quality
A practical guide to LLM inference cost optimization in production — covering model routing, prompt caching, semantic caching, quantization, batch inference, context compression, output length control, and OSS models for narrow tasks.
URL: https://prodinit.com/blog/llm-cost-optimization-production

### Why Your RAG Pipeline Is Failing in Production (And How to Fix It)
A diagnostic guide to the 5 most common RAG pipeline failures in production — bad chunking, missing reranking, stale indexes, no hybrid retrieval, and no eval loop — with code snippets and fixes for each.
URL: https://prodinit.com/blog/rag-pipeline-debugging-production

### Building Production Voice AI Agents: Latency, Architecture, and What Nobody Tells You
Why voice AI agents fail in production has nothing to do with model quality — it is the architecture. A complete guide to latency budget, WebRTC transport, LiveKit SFU, security, and observability for voice AI at 2000+ calls per day.
URL: https://prodinit.com/blog/production-voice-ai-agents-latency-architecture

### How to Evaluate LLM Outputs: Building Evals That Actually Catch Regressions
A hands-on guide to building LLM evaluations that catch silent regressions — the three failure modes of naive evals, the four-layer eval stack, golden dataset rot, LLM-as-judge bias, and how to wire evals into CI.
URL: https://prodinit.com/blog/llm-evals

### Resource Planning for AI Development Teams: A Practical Guide
A practical guide to resource planning for AI consulting and development teams — covering role composition, capacity planning across simultaneous engagements, hiring vs. contracting decisions, and retaining AI talent.
URL: https://prodinit.com/blog/ai-team-resource-planning

### How to Estimate AI Development Timelines Without Overpromising
A practical guide to estimating AI development timelines — covering work decomposition, buffer calculation, the five most common AI estimation traps, and how to communicate estimates without overpromising.
URL: https://prodinit.com/blog/ai-project-timeline-estimation

### How to Manage AI Consulting Projects That Actually Deliver on Time
A practical guide to managing AI consulting engagements from scoping to handoff — covering discovery phases, sprint planning, scope control, and leadership principles that keep complex AI projects on track.
URL: https://prodinit.com/blog/ai-consulting-project-management

### How to Deploy on Air-Gapped AWS EKS for Regulated Financial Services
A practical engineering guide to deploying containerized applications on AWS EKS inside a fully air-gapped VPC — covering network isolation, private registries, CI/CD pipelines, and secrets management for regulated financial services environments.
URL: https://prodinit.com/blog/air-gapped-eks-deployment-fintech

## Partnerships

### Cloudonix Implementation Partner
Prodinit is a Cloudonix implementation partner. We build production AI voice agents on Cloudonix — SIP trunking, CXML call flows, and LiveKit, VAPI, Retell, and ElevenLabs integrations.
URL: https://prodinit.com/cloudonix-implementation-partner

## Contact

Website: https://prodinit.com
Contact: https://prodinit.com/contact
Email: dishant@prodinit.com