®LLMs, wired into the software you already have.
We integrate frontier and open-source language models into your existing app — classification, summarisation, drafting, extraction, code-gen. Streaming UI, structured outputs, evals, and a vendor-neutral architecture so you can swap models as they improve.
When to hire us
- You want to add AI features to existing software
- You've experimented with the OpenAI API and need to ship to production
- You need structured outputs (JSON, function-calling), not just chat
- You want vendor-neutral architecture (swap Claude ↔ GPT ↔ open models)
What is LLM integration?
LLM integration is the process of adding large language model capabilities — text generation, classification, summarisation, data extraction, question answering — to existing software. For most businesses, this means connecting their existing web application, internal tool, or data pipeline to an LLM API (Claude from Anthropic, GPT-4o from OpenAI, Gemini from Google) and handling the engineering work that separates a working prototype from a production feature: streaming responses, structured JSON outputs, error handling, rate limiting, cost monitoring, and evaluation. A prototype that works in a Jupyter notebook and a feature that runs reliably for 10,000 users per day are different engineering problems, and the gap between them is where most AI features stall.
Nowgray integrates LLMs into existing products and builds new AI-native features for businesses across India and internationally. Typical projects include: adding a summarisation feature to a legal document management system, building a classification layer that routes customer support tickets by topic and urgency, creating a drafting assistant inside a CRM that generates follow-up email drafts based on call notes, and extracting structured data (SKU, quantity, price) from unstructured purchase order PDFs. Each integration is designed around the specific output format the downstream system expects — we use structured outputs (JSON mode, function-calling, tool-use) so the LLM's output flows cleanly into your existing code without parsing workarounds.
One of the most important decisions in LLM integration is avoiding model lock-in. We build with an abstraction layer that lets you switch between Claude, GPT, and Gemini with a configuration change rather than a rewrite — relevant because the model landscape changes rapidly and the best option for your use case today may be different in six months. We also implement evaluation pipelines: test sets that measure quality, latency, and cost for each task, so a model upgrade is a validated improvement rather than a risky change. LLM costs in production are tracked per request and per task, giving you the data to make informed decisions about which features are worth scaling.
The capabilities, spelled out.
Provider integration
Anthropic, OpenAI, Google, Mistral, AWS Bedrock, Azure OpenAI, Together, Groq — pick the right model per use case.
Structured outputs
JSON-mode, function-calling, tool-use, schemas with validation. AI output that flows cleanly into your code.
Streaming UI
Token-by-token streaming with proper cancel + retry handling. Feels fast, fails gracefully.
Vendor-neutral architecture
Abstraction layer so you can swap models. New cheaper or better model lands tomorrow — drop it in without rewriting.
Safety & cost control
Rate limits, spend caps, content filters, PII redaction, prompt-injection defences. Production-grade.
Evals & observability
Test prompts before deploy. Track token costs, latency, quality scores in production. No silent regressions.
Our default stack.
We'll pick the right tools for your project — but if you don't care, this is what we usually reach for.
Outcomes, not just hours.
- LLM features in production within weeks, not quarters
- Costs measurable and predictable per request
- Quality measurable — you know when AI changes regress before users do
- Architecture survives model changes — no rewrite when GPT-6 ships
Let's scope your add claude, gpt, gemini to your product.
Tell us what you have in mind — we'll come back with a clear plan, timeline, and quote.