LLM Integration & Fine-Tuning Services

We Integrate & Fine-Tune LLMs for Production

Our LLM integration and fine-tuning services help enterprises deploy production-grade language models that are accurate, fast, and cost-efficient. From API integration to custom fine-tuning with RLHF, we handle the full model lifecycle — so your team can focus on building great products.

LLM API Integration

Connect your products to leading LLM providers (OpenAI, Anthropic, Google, Cohere) with production-ready API layers, rate limiting, and fallback logic.

Fine-Tuning & RLHF

Adapt foundation models to your domain using supervised fine-tuning and Reinforcement Learning from Human Feedback for precise, aligned outputs.

Prompt Engineering & Optimisation

Design, test, and systematically optimise prompts and chain architectures to maximise accuracy and minimise token costs at scale.

Model Evaluation & Testing

Benchmark model performance with custom evals, red-teaming, and regression testing to ensure safety, accuracy, and consistency.

LLM Monitoring & Cost Optimisation

Track token usage, latency, hallucination rates, and model drift in production — with automated cost controls and retraining triggers.
50+

LLMs Integrated

in LLM integrations

99%

Uptime SLA

in fine-tuning projects

3x

Faster Inference

in AI model deployments

60%

Cost Reduction

LLM Integration

Technology

LLM Providers & Tools We Work With

our process

Our LLM Integration Process

We follow a structured, proven approach to deliver reliable, scalable LLM-powered solutions

Requirements & Use Case Definition

We work closely with your team to define clear use cases, map data sources, and establish success metrics before touching a single model.

Model Selection & Evaluation

We evaluate foundation models (GPT-4, Claude, Llama, Mistral, Gemini) against your specific requirements — balancing performance, latency, and cost.

Integration Architecture Design

We design scalable integration architecture including API gateways, context management, caching layers, and fallback strategies for production reliability.

Prompt Engineering

We craft and systematically test prompts and chains to maximise model accuracy and consistency across your specific domain and tasks.

Fine-Tuning & Training

Using your proprietary data, we fine-tune foundation models with supervised learning and RLHF techniques to align model behaviour with your business goals.

Testing & Validation

Rigorous evaluation using custom benchmarks, red-teaming, and A/B testing ensures the model meets accuracy, safety, and latency requirements before launch.

Deployment & Monitoring

We deploy models to production with full observability — monitoring hallucinations, latency, token costs, and drift — with automated alerts and retraining pipelines.

Industries We Serve with LLM Solutions

We have deep expertise across the full LLM stack — from foundation model selection and fine-tuning to RAG pipelines, prompt engineering, and production monitoring — delivering measurable improvements in accuracy, speed, and cost.

We are committed to building LLM solutions that fit your unique industry context. Whether you need domain-specific fine-tuning for legal, healthcare, or financial services, we tailor every model to your data and workflows.

End-to-end ownership from architecture through to production, with deep expertise in both open-source and proprietary LLMs.

Expertise

Leverage our deep expertise in LLM integration, prompt engineering, and domain-specific fine-tuning to embed large language models into your products with precision.

Predictable Costs

Enjoy transparent, milestone-based pricing for your LLM integration and fine-tuning engagements — clear performance targets, defined deliverables, and measurable quality benchmarks.

Tailored Solutions

Build custom LLM integrations and fine-tuned models designed around your domain data, use case requirements, and quality standards — not off-the-shelf wrappers.

Proven Track Record

Trusted by enterprises for delivering production-ready LLM integrations that enhance product intelligence and automate complex language-driven tasks at scale.

Our expertise spans the full LLM lifecycle — from model selection and fine-tuning to deployment and continuous optimisation

Our Recent Projects

A quick glance at some of our notable application development projects that showcase our commitment to quality and innovation.

01.
Showcase
Convenience and learning, all at your fingertips
02.
Showcase
Your trusted partner for hassle-free legal solutions, right at your fingertips
03.
Showcase
Bite-sized learning modules for easy absorption and retention

Our Client Testimonials

Discover how our tailored software services helped our partners in driving business transformation.

David Morandes
CEO & Founder, Skole App
4.9
Rated 4.5 out of 5

Got a project?

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FAQ

Frequently Asked Questions

Custom software development is the process of designing, creating, deploying, and maintaining software for a specific set of users, functions, or organizations. Unlike off-the-shelf software, custom software is tailored to address particular preferences and requirements.

Opting for custom software development provides numerous benefits that off-the-shelf solutions simply can’t match. It offers unparalleled flexibility, allowing your software to evolve alongside your business needs. Moreover, custom software can be seamlessly integrated with your existing systems, improving overall efficiency and productivity.

The cost of custom software development can vary significantly based on several factors. These include the complexity of the project, the number and intricacy of features required, the size of the development team, and the timeline for completion. A small to medium-sized project might range from $50,000 to $250,000, while complex enterprise software development could cost anywhere from $250,000 to $1 million or more.

The technologies used to build bespoke software solutions are chosen based on the specific requirements of your project, your existing technology stack, and the latest industry standards. Common programming languages include Java, Python, C#, and JavaScript, while frameworks like, NET, Angular, and React are often employed for web-based applications. For database management, SQL Server, MySQL, or MongoDB are used depending on your data structure needs. Cloud platforms like AWS, Azure, or Google Cloud are frequently utilized for hosting and scalability. At Sthambh, we stay abreast of emerging technologies and can incorporate cutting-edge solutions like AI and machine learning where appropriate.

Data confidentiality is a top priority for us as a top software development company. We implement robust security measures at every stage of development, adhering to industry-standard security protocols and ensuring compliance with regulations like GDPR, HIPAA, or CCPA as needed.

The timeline for custom software development varies. A small to medium-sized project might take 3 to 6 months, while larger, more complex systems could require 9 to 18 months or more. At Sthambh, we use agile development methodologies which allow for flexibility and iterative progress.

Sthambh’s custom development service stands out due to our combination of technical expertise, business acumen, and client-centric approach. Our team of seasoned developers brings a wealth of experience across various industries, allowing us to understand and address your unique challenges.

Our software development team prioritizes the security-first approach by following the standard coding standards to build robust and highly secure solutions. Also, to ensure scalability, we leverage cloud-native technologies such as AWS, enabling the software to scale as per requirements.

Yes, our experienced developers are well-versed in integrating custom software solutions to the existing systems. The solutions we develop for you are crafted in a way that they go along with your existing databases and third-party applications.

Being a professional software development company, we offer comprehensive support and maintenance services not just after deployment but post-launch, as well, ensuring that the developed solution continues to function seamlessly and meets your evolving needs.

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LLM Integration for Enterprise: A Practical Guide (2026)

Table of Contents Seventy-nine percent of enterprises now report challenges adopting AI — a double-digit jump from 2025. The problem is not the models. GPT-4o, Claude, Gemini, and a growing roster of open-source alternatives are all capable enough for production use. The problem is LLM integration enterprise teams keep getting

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