Technology · AI
Connects your product to AI language models from several providers, so each task uses a suitable model and you can switch later.
How we use it
We build integrations that can route between Anthropic Claude, Google Gemini, OpenAI and open-weight models such as Llama or Mistral, so no AI feature depends on a single provider.
A large language model (LLM) is the kind of AI behind chat assistants: it reads and writes text. Several companies offer them, including Anthropic, Google and OpenAI, and some open models can run on your own servers. LLM integration means connecting your product to one or more of these in a way you can change later. For you, it avoids betting the product on a single provider's prices and terms.
Models differ in reasoning quality, speed, context length, language coverage and price, and their relative standing shifts every few months. A thin abstraction layer lets a feature switch models, or use different ones per task, without a rewrite. For Indian-language features we compare general models with Indic-focused options, such as AI4Bharat's open models or the government's Bhashini services, using test sets written the way your users write.
Use cases
Faster, cheaper models for simple steps; stronger ones for complex reasoning.
Answers grounded in your documents through vector search.
Private deployments in your cloud for sensitive data.
Hindi, Tamil and other languages, checked by native speakers.
Multi-step tasks that call your tools, with sign-off on actions.
Switch providers if pricing, terms or quality change.
Pay for a stronger model only where the task needs it.
Hosting chosen to fit your data obligations.
One test set scores every candidate model, so choices rest on evidence.
Plain-English glossary
Services
AI features and AI-first apps built on model APIs, retrieval over your own data, workflow automation or custom ML, with evaluation and human review.
Software shaped around how your business runs, from approvals and inventory to billing and reports, replacing spreadsheets and disconnected tools.
Multi-tenant SaaS products with subscription billing, team roles, onboarding and usage analytics, from first MVP to paying customers.
Solutions
Automations for repetitive back-office work, with AI steps that read, classify and draft, and approvals where decisions matter.
Assistants that book, schedule, look up records and raise requests by voice or text, with confirmation before every action.
Search and question-answering over your documents, tickets and product data, with hybrid retrieval, citations and permission-aware results.
Multi-tenant SaaS products with AI at their core, built with tenant isolation, usage metering, model routing, evaluation and subscription billing.
Features that generate catalogue copy, reports, summaries, translations and images, with brand controls and a review step before anything is published.
Work
Illustrative projects that show how we plan and build products that use LLM integrations. They are samples, not client work.
AI solutions
An illustrative AI assistant that answers order, return and product questions from a D2C brand's own policies and order data, with sources and a human hand-off.
Runs on
Built with
Industries
Industries whose typical builds with us include LLM integrations.
FAQ
Use an API unless you have a clear reason not to. APIs are quicker to start with, need no GPUs and are maintained by the provider. Self-hosting makes sense when data must stay within your infrastructure, volumes are high and steady enough to justify dedicated hardware, or you need a fine-tuned model you control.
Quality varies by model, language and task. Larger general models handle Hindi reasonably well, and other Indian languages with more variation, especially informal or transliterated text such as Hinglish. We test candidate models on real examples from your users, have native speakers check the answers and add translation or Indic-specific models where they perform better.
An agentic workflow lets a model plan steps and call tools, such as looking up an order, drafting a refund or updating a CRM record. It is safer when permissions are narrow and consequential actions need human approval. We limit which tools an agent can use, log every step, set spending and rate limits, and start with read-only actions before allowing changes.
Next step
Share a few real examples of the task. We'll test candidate models and explain the trade-offs.
