Should we fine-tune or use RAG?

Start with retrieval and prompting: it is cheaper, faster to change and easier to audit. Fine-tune when you need a specific style, format or domain behavior that prompting cannot deliver reliably, or when cost per call must drop. We test both against the same evaluation set before recommending.

How do you stop the model from making things up?

Grounding through retrieval with citations, constrained output formats, confidence thresholds with graceful fallbacks, and evaluation sets that specifically test for unsupported claims. Hallucination is measured, not assumed away.

Can this run on our private cloud or on-premises?

Yes. We deploy open-weight models on your infrastructure or use private endpoints from cloud providers so data never leaves your perimeter.

Will the assistant work on WhatsApp, web chat and voice at once?

Yes. We build channel-agnostic assistants with a shared brain and channel-specific rendering for web, mobile, WhatsApp, SMS, voice and messaging platforms.

Will AI integration slow down or destabilize our product?

Not when it is designed as a separate, resilient layer with timeouts, fallbacks and feature flags. Existing flows continue to work if the AI component is unavailable.

AI Engineering

Generative AI & LLM Applications

LLM applications, RAG, conversational AI and AI features inside the products you already run.

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See related work

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Overview

Generative AI development is mostly engineering. The model is a component; the product is the retrieval design, the prompt and context strategy, the evaluation suite, the guardrails and the cost controls wrapped around it. We build all of it, and we build it to be measured.

Our teams have shipped LLM features for search, summarization, drafting, extraction and question answering over private data. The pattern is always the same: define what good looks like, build an evaluation set from your real data, then iterate on retrieval, prompts and models until the numbers hold up under adversarial testing.

This page also covers conversational AI, integration of AI into existing software and AI-native product development, because these are variations on the same engineering discipline.

Offering 1

LLM applications and RAG

We build LLM features for search, summarization, drafting, extraction and question answering over private data, grounded in retrieval and measured against an evaluation set built from real examples.

LLM application development

Assistants, copilots, drafting and summarization features built into web, mobile and enterprise products.

Retrieval-augmented generation (RAG)

Document ingestion, chunking, embedding, hybrid search, reranking and citation so answers are grounded in your content.

Evaluation and testing

Golden datasets, LLM-as-judge and human review workflows, regression suites and adversarial tests run in CI.

Guardrails and safety

Input and output filtering, PII handling, prompt-injection defenses, policy enforcement and fallbacks.

Typical use

Knowledge assistants over policies, manuals, tickets and contracts
Search that answers questions instead of returning ten links
Extraction of fields from invoices, claims, onboarding forms and legal documents

Offering 2

NLP and conversational AI

Most of a company's knowledge lives in text: tickets, emails, contracts, notes, chats, calls. We build assistants and language pipelines that make that text searchable, summarizable and actionable.

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Conversational assistants

LLM-based chat and voice assistants for customer service, sales, HR and IT, integrated with your systems of record.

Voice AI

Speech-to-text, text-to-speech and telephony integration for contact centers and field operations.

Document intelligence

Classification, entity extraction, clause detection and summarization across large document sets.

Multilingual NLP

Support for English, Indian languages, Arabic and other languages across markets you serve.

Typical use

Customer support assistants that resolve common requests and summarize the rest for agents
Employee help desks for HR, IT and policy questions
Automated triage and routing of tickets and emails

Offering 3

AI integration into existing software

You do not need a new product to benefit from AI. AI integration adds capabilities like answering questions, drafting and flagging risk to existing software without rewriting what works.

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AI features in existing products

Assistants, smart search, recommendations, summarization and automation inside your current web and mobile applications.

Enterprise system integration

AI capabilities connected to ERP, CRM, ITSM, HRMS and document platforms through APIs and events.

API and middleware layers

Secure AI gateways that handle authentication, rate limiting, routing between providers, logging and cost allocation.

Legacy system enablement

Wrapping older systems with APIs and data pipelines so AI can read from and write to them safely.

Typical use

Adding an assistant to a SaaS product without changing its core architecture
Smart routing and summarization in ticketing and CRM systems
An AI gateway that gives every internal team governed access to models

Offering 4

AI-native product development

Some ideas cannot be bolted onto an existing product. They need to be built as AI-native products from the start, with the model, the data flywheel, the experience and the economics designed together.

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Product discovery for AI

Problem validation, user research, feasibility on real data and a scoped MVP definition.

AI-native architecture

Model strategy, retrieval and data design, evaluation and cost modeling built into the foundation.

Experience design

Interfaces for uncertainty: confidence, citations, corrections and control that keep users trusting the product.

Data flywheel design

Capturing feedback and outcomes so the product improves with use.

Typical use

Vertical AI SaaS for legal, HR, healthcare, finance or logistics workflows
Copilot products for specialized professions
New AI product lines inside established software companies

Want this for your product? Talk to an engineer who has built it.

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How we work

How we deliver

01
Define
Agree the task, the users, the quality bar and the evaluation set built from real examples.
02
Prototype
A working prototype on your data within weeks, with baseline scores on the evaluation set.
03
Harden
Guardrails, observability, cost controls, security review and integration into the product.
04
Operate
Launch with monitoring, feedback capture and a cadence for prompt, retrieval and model updates.