How do you keep AI features from hallucinating in production?

Grounding (RAG with citations), constrained outputs (schemas and validators), evaluation suites that run before every release, and human-in-the-loop checkpoints for high-stakes actions. AI output that can't be verified doesn't ship to production.

Services · AI

AI product development, engineered.

Q: How long does it take to build an AI product?

A focused AI MVP typically ships in 4–6 weeks through our MVP Sprint. An AI Transformation engagement — turning existing business workflows into AI-assisted ones — runs 6–12 weeks depending on how many systems we integrate.

Q: Which AI models do you work with?

We integrate OpenAI (GPT models), Anthropic Claude and Google AI (Gemini), and we choose per use case — reasoning depth, latency, context size and cost differ meaningfully between them. Most production systems we build can switch providers behind one interface.

Q: What is RAG and do I need it?

Retrieval-Augmented Generation grounds a language model in your own data: documents are indexed, relevant passages are retrieved per query, and the model answers from them with citations. You need it when answers must reflect your private, current knowledge rather than the model's training data.

Q: What does an AI product cost to run?

Model usage is a real operating cost, so we engineer for it from day one: caching, model routing (small models for simple calls), token budgets per feature and per-tenant metering. You see projected unit economics before we build.

Most AI features die between the demo and the deploy. Mokshify builds LLM applications, RAG systems and AI agents the way we build everything else — as production systems with tests, evaluation, guardrails, observability and a cost model — on OpenAI, Anthropic Claude and Google AI.

What we build

LLM applications

Products where a language model does real work: drafting, summarising, extracting, classifying, answering. We design the prompt and context architecture, the fallback behaviour, and the interface that makes model output trustworthy to a user — including confidence signals and citations where they matter.

RAG systems

Retrieval-Augmented Generation grounds the model in your data. We build the full pipeline: document ingestion and chunking, embeddings and vector search, retrieval tuning, answer synthesis with citations, and the evaluation harness that tells you — with numbers — whether answers got better or worse after a change.

AI agents and workflow automation

Agents that take multi-step actions against your systems: reading tickets, updating records, drafting responses, escalating to humans. The engineering that matters here is boring on purpose — permission boundaries, audit logs, idempotent actions and human checkpoints — because an agent without them is a liability, not a feature.

ChatGPT, Claude and Gemini integration

Adding intelligence to an existing product: an assistant inside your SaaS, document understanding inside your operations tool, natural-language search over your catalogue. We integrate against OpenAI, Anthropic and Google APIs behind one internal interface, so you can switch or mix providers as pricing and capability shift.

How an AI build actually runs

AI work follows our standard eight-stage pipeline, with three additions specific to machine intelligence:

Feasibility before commitment. Week one produces a working spike against your real data — not a slide. If the model can't do the job reliably, you find out for the price of a week, not a quarter.
Evaluation as a first-class artifact. Every AI feature gets a test set of real cases and a scoring harness. Releases are gated on eval results the same way code is gated on tests. Our own deploy pipeline runs an AI review step on every release — we hold our tooling to the same bar.
Cost engineering. Token spend is modelled per feature before launch: caching strategy, model routing (small model first, big model on escalation), context budgets and per-tenant metering, so growth doesn't surprise your margins.

The stack underneath

AI features are only as reliable as the platform they run on. Ours is the same production platform we use for every build: Python and FastAPI or Node.js services, PostgreSQL (with pgvector for embeddings) and Redis, deployed with Docker, Kubernetes and Terraform through GitHub Actions onto AWS, Azure, Oracle Cloud or Google Cloud — see Cloud Engineering & DevOps.

Proof, not promises

We're a studio that ships: Medico Diagnostics (3,291+ tests published, 4–6 hour reporting), FinCalix (30+ calculators), Terravion Properties and SkillForceHub are live products, not mockups. The same delivery discipline — staged rollouts, tests, AI-gated releases — carries every AI engagement.

Common questions

How long does it take to build an AI product?

A focused AI MVP ships in 4–6 weeks (MVP Sprint). An AI Transformation — converting existing workflows to AI-assisted ones — runs 6–12 weeks depending on how many systems we touch.

Which AI models do you work with?

OpenAI, Anthropic Claude and Google AI (Gemini). We choose per use case — reasoning depth, latency, context window and cost differ meaningfully — and we usually build behind one internal interface so you can switch providers later.

What is RAG, and do I need it?

Retrieval-Augmented Generation grounds a model in your own documents and data, with citations. You need it when answers must reflect private, current knowledge — policies, catalogues, records — rather than what the model memorised in training.

How do you prevent hallucinations in production?

Grounding via RAG, schema-constrained outputs with validators, evaluation suites gating every release, and human checkpoints on high-stakes actions. If we can't verify it, it doesn't ship.

What does an AI product cost to run?

We model token spend per feature before building: caching, model routing, context budgets, per-tenant metering. You see projected unit economics up front, not on your first cloud bill.

Have an AI use case in mind?

Send one paragraph about the workflow or product. Within 24 hours you'll get an honest feasibility read and a suggested first step — sometimes that's a one-week spike, not a contract.

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