LLM Integration & AI Development
Embed the power of GPT-4, Claude, and Gemini directly into your business workflows.
Off-the-shelf AI tools are generic. Your business has unique data, proprietary context, and processes that generic AI cannot understand. Akranit integrates leading LLMs — GPT-4o, Claude 3.5, Gemini Pro, and open-source models — directly into your existing applications, internal tools, and customer-facing products. The result is AI that speaks your language, knows your data, and delivers decisions tailored to your business context — not a one-size-fits-all chatbot.
What You Get
Everything in One Integrated System
Model Selection & Architecture Design
We select the optimal LLM for your use case — balancing accuracy, latency, cost, and data privacy — across GPT-4o, Claude 3.5 Sonnet, Gemini Pro, Llama 3, and Mistral.
Retrieval-Augmented Generation (RAG)
We build RAG pipelines that give the LLM access to your proprietary data — documents, databases, product catalogs — in real time, without exposing sensitive data to model training.
Fine-Tuning on Proprietary Data
For specialized use cases, we fine-tune base models on your domain data to achieve accuracy levels that prompted-only models cannot match, particularly for classification, extraction, and generation tasks.
Prompt Engineering & Guardrails
We engineer system prompts, output schemas, and validation layers that ensure consistent, structured, on-brand responses — eliminating hallucination risk in production environments.
LLM API Integration
We embed AI capabilities into your existing web apps, internal tools, CRMs, and workflows via clean API layers — no rearchitecting your stack required.
Cost & Latency Optimization
We implement caching, model routing, token optimization, and batching strategies that reduce LLM API costs by an average of 40% while improving response speed.
How It Works
Live in Weeks, Not Months
Use Case Definition
We document the specific AI capability you need, the data inputs available, the expected output format, and the success metrics — turning a vague 'AI idea' into a buildable specification.
Architecture & Model Selection
We design the technical architecture — RAG vs. fine-tuning vs. prompted model, vector database selection, API design — and present a cost-performance model for your approval.
Development & Evaluation
We build the integration, run structured evaluations against 200+ test cases covering accuracy, edge cases, and adversarial inputs, and iterate until benchmarks are met.
Production Deployment & Monitoring
We deploy to your infrastructure with logging, rate limiting, cost dashboards, and performance monitoring — and provide ongoing model updates as base models improve.
Seen enough? Let's build your system.
Free 30-min consultation — no commitment, just a clear plan.
Real Numbers
The ROI Speaks for Itself
LLM-powered document extraction, summarization, and classification processes in seconds what previously took hours of human review.
Fine-tuned and RAG-augmented models achieve 90–96% accuracy on specialized business tasks, versus 70–75% for generic prompted models.
Our optimization strategies — caching, model routing, and token efficiency — reduce average monthly LLM API spend by 40% compared to naive integrations.
Common Questions
Before You Book a Call
Let's Build This in Your Business
Book a free 30-minute call. We'll map out exactly how llm integration & ai development fits your workflow — with a deployment plan and ROI estimate upfront.
Integrate AI Into My Stack