Trusted by companies across the USA
Connect large language models to the workflows your business already runs on.
Most LLM projects stall because the model is impressive in a demo but disconnected from the actual data and processes that run the business. We build the integration layer that makes AI useful, not just interesting. That means connecting OpenAI, Claude, or Llama to your databases, APIs, and user interfaces in a way your team can actually use.
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Trusted by companies across the USA
A staffing company we worked with had a two-person team spending 11 hours a week reviewing resumes and writing candidate summaries for clients. They had tried a generic AI tool, but it kept hallucinating credentials and producing summaries that had to be rewritten anyway. The problem was not the model. It was that the model had no connection to their internal candidate database, their client requirement templates, or their scoring criteria. Once we built a structured integration using the OpenAI API tied directly to their existing MySQL database and a Node.js API layer, that 11-hour task dropped to under 90 minutes, and the outputs were accurate enough to send to clients without manual review.
That is what LLM integration development actually means. It is not dropping a chatbot onto your website. It is building the architecture that lets a language model read your data, follow your business rules, and produce output that fits into your existing workflow. This involves prompt engineering, retrieval-augmented generation, API design, and in some cases fine-tuning or model selection across providers like OpenAI, Anthropic's Claude, or self-hosted Llama instances via Python. The model is one piece. The plumbing around it is where most of the real work happens.
We have been building software for US businesses since 2015, and LLM integration is now one of the most requested services we get. Our team is based in Gandhinagar, India, which means your project is moving while you sleep. You send a question or a review comment at the end of your business day and wake up to a response, a demo recording, or a pull request. It is a working model that our clients across 20+ countries have found genuinely useful, not just tolerable.
We scope LLM integrations tightly so you see a functional build within 3-4 weeks. You can test it against real data before committing to a full rollout.
Every project is quoted at a fixed price before work starts. If the scope changes, we agree on it in writing. You never open an invoice wondering what happened.
We build caching, batching, and retrieval layers specifically to reduce token consumption. One client cut their OpenAI API spend by 38% after we restructured how their prompts were assembled.
We connect LLM outputs to the tools you already use, whether that is a MySQL database, a REST API, a Slack workspace, or a web portal you built years ago.
All source code, prompts, and fine-tuning data belong to you at handoff. We sign an NDA before discovery starts and transfer full IP ownership at project close.
GPT-4o is not always the right call. For some tasks, Claude handles long documents better. For others, a self-hosted Llama model makes more sense for privacy or cost reasons. We tell you which and why.
We build conversational interfaces connected to your actual business data, not generic FAQ bots. These handle real queries using your product catalog, knowledge base, or CRM records.
Contracts, reports, support tickets, and research documents processed and summarized automatically. We build the extraction and routing logic so the right information reaches the right person.
Retrieval-augmented generation systems that let a language model answer questions using your private data without exposing it to the model during training. Accurate, auditable, and scoped to what you actually need.
We connect OpenAI, Claude, or Llama endpoints to your backend via structured API layers. This includes fallback logic, rate limiting, and response validation so your app does not break when a model misbehaves.
Repetitive internal tasks like drafting, classification, data entry, or routing can often be handed to a language model. We map your workflow first, then build the automation around the parts that actually benefit from it.
When a base model is too generic for your use case, we handle structured fine-tuning on your domain data or engineer a prompt system that produces consistent, reliable outputs across varied inputs.
No 47-slide proposal deck. No three-month discovery phase. Here is how a project moves from your idea to working software.
Start Your ProjectWe spend the first week understanding the workflow you want to improve, not the technology you want to use. We review your existing data sources, API access, and the specific outputs the integration needs to produce. The goal is a written spec that describes exactly what the AI does, what it reads, and what happens with the result.
If the integration needs a user-facing interface, we design it around how your team actually works. That might be a chat panel inside an existing tool, a review queue with AI-generated suggestions, or a simple API your developers call. We prototype the interaction flow before writing backend code.
We build the integration in Python or Node.js depending on your stack, wire the model API to your data sources, and set up the prompt templates, context management, and output parsing. We use Docker to keep the environment consistent across development, staging, and production.
LLM outputs are probabilistic, which means testing is different from standard software QA. We run the integration against real edge-case inputs from your data, validate that outputs meet your defined quality bar, and test failure handling when the model returns something unexpected.
We deploy to your environment, confirm monitoring is in place for both application errors and API usage costs, and run a live walkthrough with your team via Zoom. You get documentation covering how the prompts are structured and how to adjust them if your needs change.
After launch, we monitor performance for the first 30 days and address any issues within one business day. If your usage grows or the model needs retuning as your data changes, we offer a retainer structure for ongoing updates, prompt revisions, and model upgrades.
Our team is based in Gandhinagar, India. When your workday ends, ours is starting. Most clients find they wake up to progress, recorded demos via Loom, or questions that keep the project from stalling.
We do not rotate you through a support queue. The engineers who build your integration are the ones you talk to throughout the project. They know your data structure and your edge cases without needing a briefing every call.
We have been building custom software for US businesses for over 11 years. LLM integration is a newer discipline, but the underlying work of connecting APIs, managing data pipelines, and shipping reliable software is not.
We run daily Slack updates and schedule Zoom calls around US Eastern and Pacific hours. Nothing critical waits for a time zone to catch up. We use shared project boards so you always know what is being worked on.
Remote delivery is not an experiment for us. We have shipped software for clients across North America, Europe, and Australia using the same communication practices we use today. The workflow is proven.
We sign a non-disclosure agreement before any discovery conversation happens. All code, prompt templates, and training data you provide belong to you. There is no licensing clause or revenue share buried in the contract.
Common questions about llm integration development.
Tell us what your team does manually today and where it breaks down. We will review it and show you specifically what an LLM integration could do, and whether it is actually worth building.
Include as much detail as you want. We typically reply within 24 hours.