Custom LLM applications, RAG copilots, fine-tuned models and private AI deployments — engineered to be accurate, secure and affordable at production scale.
Generative AI is easy to demo and hard to run in production. Answers drift, costs spike, sensitive data leaks into prompts, and a prototype that impressed the board falls apart with real users. Closing that gap is what we do.
ArrLink provides generative AI development services for companies that want GenAI to deliver measurable outcomes — faster support, faster document processing, better search, new AI-powered products. We build on leading models like Claude, GPT and Gemini as well as open-source models you can host privately, and we ground every application in your own data.
Every system we ship comes with evaluation datasets, guardrails, cost controls and monitoring, so you know how accurate it is, what it costs per request and how it behaves when something unexpected happens.
Assistants that answer from your documents, databases and tickets with citations — and say 'I don't know' instead of guessing.
Purpose-built GenAI products: content generation, summarization, extraction, classification and analysis tools.
Extract structured data from contracts, invoices, medical records and forms with human review where it matters.
Fine-tuned and distilled models for domain language, tone or cost reduction when prompting isn't enough.
Open-source models deployed in your cloud or data center for data sovereignty and regulatory compliance.
Use-case prioritization, build-vs-buy analysis and audits of existing GenAI systems for accuracy, cost and risk.
Contract review, clause extraction and policy Q&A with traceable sources.
Clinical documentation, medical record summarization and diagnostic support tools.
Copilots that draft accurate replies for support teams from your knowledge base.
One search box across Drive, Notion, Confluence, Slack and your databases.
We assess where GenAI will pay off, what data is available and what accuracy is required.
We build a test set of real questions and expected answers before writing the application, so quality is measurable.
Retrieval pipelines, prompts, tools and UI developed in weekly cycles against the evaluation set.
Guardrails, PII handling, caching, rate limits, cost controls and access controls.
Observability for quality, latency and spend, with regular re-evaluation as models and data change.
We've delivered AI for diagnostics and ECG analysis, where accuracy isn't optional — the same discipline goes into every project.
Every release is scored against an evaluation set, so you see accuracy and cost numbers instead of anecdotes.
Private deployments, no training on your data, and architecture designed around your compliance requirements.
ArrLink product
A multi-tenant support desk that puts WhatsApp, Instagram, Messenger, Telegram, email, live chat and phone calls in one inbox — with an AI assistant that summarises calls, fills in lead details and replies in the customer's Hinglish.
Visit desk.arrlink.comSynergylabs
An AI platform that turns a single prompt into a fully deployed, production-ready application — from generated code to hosting.
Krinos AI
The AI backbone of a HIPAA-compliant oral-healthcare platform, spanning CBCT scan analysis through to automated insurance claims.
Mythyaverse
A clinically precise, production-ready model that detects cardiac arrhythmias from ECG signals, built against strict medical requirements.
They cover designing, building and running applications powered by large language models and other generative models — for example RAG copilots, document processing, content generation and AI-powered product features — including the data, evaluation and infrastructure around them.
Retrieval-augmented generation (RAG) lets an LLM answer using your own documents and data instead of only its training data. It makes answers more accurate and current, and it allows the AI to cite its sources.
Yes. We deploy open-source models such as Llama and Mistral in your own cloud account or on-premise infrastructure when data residency, privacy or cost requires it.
It depends on accuracy, latency, cost and privacy needs. We benchmark candidate models on your own evaluation set and recommend the best fit — often a mix of models for different tasks.
By grounding answers in retrieved data, constraining outputs, requiring citations, adding verification steps and measuring hallucination rates on an evaluation set before every release.
Free Strategy Call
Tell us what you're trying to build. In one 30-minute call we'll map the scope, the fastest path to production and a tailored proposal — no obligation.
