Garud Labs

AI product development for companies shipping real software

We design and build custom AI features inside your product — the retrieval layer, the data pipeline underneath it, the evaluation harness that proves it works, and the monitoring that keeps it affordable in production. Engineering work, delivered by a team that stays with it after launch.

To be clear about what this page is offering

Searches for AI development now return a mix of very different businesses, so it is worth stating plainly which one we are.

What GarudLabs is

A software engineering studio. You hire us to build custom AI features into your business's product or internal systems. We write the code, integrate it with your stack, and are accountable for whether it works in production.

What GarudLabs is not

We are not a marketplace of pre-built AI agents, not a platform that rents you AI tooling by seat, and not a staffing service that forwards you résumés of AI engineers to manage yourself. There is no product catalogue here — the deliverable is working software in your repository.

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What we build, concretely

AI features inside an existing SaaS product

The most common request. A natural-language query layer over data the user already owns, automated drafting or summarising inside an existing workflow, classification and routing of inbound records. The constraint is integration: your auth, your tenant boundaries, your permission model, your audit log.

Retrieval over your own documents and data

Question answering grounded in your knowledge base, contracts, tickets or product data, with citations back to the source. This is the highest-value and lowest-risk starting point for most companies. We cover the architecture in depth on our RAG development page.

Data pipelines that make AI features possible

Frequently the real first project. Extracting and normalising records from the systems where they actually live, chunking and embedding documents, keeping indexes fresh as source data changes, and handling deletion properly so removed records stop appearing in results.

Evaluation and monitoring infrastructure

A test set of real cases with expected outputs, scored automatically on every change, plus production tracking of cost, latency and failure rates per request. This is what lets you change a prompt or upgrade a model without crossing your fingers.

Custom models or model APIs — how we decide

This choice drives your cost structure and your compliance position, so we make it deliberately rather than by default.

ApproachWhen it is rightThe trade-off
Hosted model APIDefault choice for product features. Strong capability immediately, no training cost, upgrade path as models improve.Per-request cost at scale; data leaves your infrastructure; you inherit the provider's rate limits and deprecations.
Open-weight model, self-hostedData residency or contractual rules prevent third-party processing, or request volume makes per-call pricing uneconomic.You own GPU capacity, deployment and upgrades. Real infrastructure work — worth it at volume, wasteful before.
Fine-tuning an existing modelOutput must follow a consistent house format or a narrow domain style that prompting keeps missing.Needs a curated training set and re-running when the base model changes. Teaches format and tone, not new facts.
Retrieval instead of trainingThe gap is knowledge, not behaviour — the model needs access to your facts.Adds retrieval infrastructure to maintain, but your data stays current without retraining.

In practice most engagements start with a hosted API plus retrieval, because it reaches production fastest and generates the usage data needed to judge whether anything more involved is justified. For the retrieval architecture in detail, see RAG development.

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How we run an AI engagement

  1. 1
    Week 1

    Find the task worth automating

    We start from a workflow, not a model. Which repeated human judgement is expensive, tolerant of a reviewable error, and already captured in your data? Many candidate features fail this test — the honest answer is often that a query, a rules engine or better search solves it without a model at all, and we will say so before you fund an AI build.

  2. 2
    Week 1–2

    Audit the data before promising anything

    We look at what you actually hold: where it lives, how it is structured, how clean it is, what is personally identifiable, and who is allowed to see which parts. This step is where AI projects quietly succeed or fail. If the data needed to support the feature does not exist yet, the first deliverable becomes a pipeline, not a model.

  3. 3
    Week 2–3

    Define what 'correct' means, and build the eval

    Before feature work, we build an evaluation set from your real cases with expected outputs, so quality becomes a number rather than a feeling. Without this you cannot tell whether a prompt change helped, and you cannot safely upgrade a model later. This is the single most-skipped step in AI projects and the reason so many stall in pilot.

  4. 4
    Week 3–6

    Thin vertical slice into your real product

    We ship one narrow feature end to end inside your actual application — with auth, permissions, logging and error states — behind a feature flag for a small user group. A slice in production teaches you more in a fortnight than a broad prototype does in a quarter.

  5. 5
    Ongoing

    Harden, instrument, then widen

    Fallbacks for model downtime, rate-limit and retry handling, cost and latency tracking per request, and human review paths for low-confidence output. Once the eval score and unit economics hold, we widen the rollout and move to the next slice.

Technology we work with

The AI layer sits on top of ordinary application engineering, so both stacks matter. These are the tools our team uses in production work.

Core Technology Stack

PythonFastAPIAnthropic APIOpenAI APILangChainLlamaIndexPostgreSQL / pgvectorPineconeNext.jsNode.jsTypeScriptFlutterDockerAWSGCP

We are not committed to a single vendor. Model providers and vector stores are chosen per project against your data residency requirements, expected volume and existing infrastructure — and we build the integration so a provider can be swapped without a rewrite.

The engineering foundation this builds on

AI features are only as reliable as the product around them. Our team's production work includes real-time video infrastructure, payment flows and multi-tenant platforms — the unglamorous engineering that AI features depend on.

Entrance Dose

A live-class education platform on iOS and Android with adaptive bitrate video, course management, payments and progress tracking. A content- and question-heavy product of exactly the kind where retrieval features have a clear application.

Flutter · PHP · MySQL · Firebase · Socket.io

See the work →

Gymtaar

A fitness platform with trainer scheduling, in-app video consultation, commerce and workout tracking — structured user data and recommendation surfaces throughout. An ongoing engagement.

Flutter · PHP · MySQL · FonePay · Agora

See the work →

We describe these as the engineering foundation rather than as AI case studies, because that is what they are. If you want to talk to us specifically about AI work in progress, ask on a call and we will be straight with you about what is shipped and what is early.

Frequently Asked Questions

It means engineering a feature that uses machine learning or a language model, inside a product that real users log into. Concretely that involves five things: the data pipeline that feeds the feature, the retrieval or model layer itself, the application code and interface around it, an evaluation harness that measures output quality, and the monitoring that tracks cost, latency and failure in production. The model is usually the smallest part of that list.

For most product features we use hosted models from providers such as Anthropic and OpenAI, because the capability is strong, there is no training cost, and you can upgrade as models improve. We use open-weight models on your own infrastructure when data residency rules prevent sending data to a third party, when per-request cost at your volume justifies it, or when latency requirements are tight. Training a model from scratch is rarely the right answer for a product feature, and we will tell you when it is not.

That is the majority of our AI work. We integrate into a live codebase rather than building a separate AI product beside it, which means respecting your existing authentication, tenant isolation, permission model and audit requirements. In a multi-tenant system the hard part is usually ensuring one tenant's data can never surface in another tenant's AI output — that constraint shapes the retrieval architecture from the start.

We instrument cost per request from the first deployment and treat it as a product metric. The practical levers are routing simpler requests to smaller models, caching repeated work, keeping retrieved context tight rather than sending everything, and setting hard budget ceilings with alerts. Cost surprises in production almost always trace back to unbounded context or a missing cache, both of which are design decisions rather than accidents.

We would rather tell you in week one than bill you for six months. A meaningful share of requests we receive are better solved with conventional software: a well-built search index, a deterministic rules engine, or fixing the data model underneath. Recommending against a model when it is not warranted costs us a project and keeps our track record honest.

Three layers. First, ground answers in your retrieved data rather than model memory, and require citations back to source documents. Second, constrain the output format and validate it in code before it reaches a user. Third, route low-confidence cases to human review instead of guessing. We also design the interface to show its sources, so users can verify rather than trust blindly — a product decision as much as a technical one.

Related services

RAG developmentSaaS developmentData engineeringDedicated development teamMVP development

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