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Getting an LLM to answer is easy. Getting it to answer correctly every time, grounded in your data, fast enough to wait for and cheap enough to run at scale — that is engineering. We build and rebuild LLM applications, RAG systems and AI agents with the architecture, evaluation and observability production actually demands.
Enterprises, SMEs and fast-growing teams trust ZTS India
LLM adoption is accelerating, but most teams hit the same wall between a working demo and a reliable, affordable production system. Click a panel to see the challenge, how we fix it, and what changes.
The model has no access to your documents, your policies or your data, so it fills the gap with something plausible. Two bad answers in front of a customer and your team quietly stops trusting it.
Your assistant answers questions beautifully and then tells the user to go and do it themselves. Every real action still needs a human, so the savings never arrive.
Latency climbs, the token bill triples in a quarter, and every request goes to the most expensive model whether it needs to or not. Nobody budgeted for the cost of success.
No traces, no quality signal, no cost breakdown, no audit trail. You find out the system degraded when a customer complains — and you cannot prove what it said or why.
Someone tweaks a prompt, the demo looks better, and three other behaviours regress silently. With no test set, "improvement" is just the last person's opinion.
We design and build production-grade LLM systems, RAG architectures and AI agents — so you can move from experimentation to something your business can actually depend on.
End-to-end LLM products engineered for production from the first commit — architecture, evaluation, guardrails and observability built alongside the features, not bolted on before launch.
Retrieval that actually retrieves the right thing. We tune chunking, embeddings and re-ranking on your own content, enforce citations, and make retrieval permission-aware per user.
Agents that complete multi-step work across your tools — with planning, scoped permissions, approval gates and a full audit trail of every action taken.
Prompts are code. We version them, test them and measure them — engineering context windows, structured outputs and system instructions that behave the same on the thousandth call as the first.
Fine-tuning when it earns its cost — and honest advice when it does not. Most accuracy problems are retrieval problems wearing a fine-tuning costume.
The difference between a demo and a product. Golden test sets, automated scoring, hallucination and bias checks, and red-teaming before anything faces a customer.
Connect LLM systems to the CRM, ERP, helpdesk and internal tools your teams already work in — securely, with SSO and role-based access, and without a rebuild.
Run LLMs like software. Tracing, quality scoring, cost dashboards and alerting so accuracy holds and spend stays predictable long after launch day.
More than a decade delivering measurable results for enterprises, SMEs and technology companies worldwide.
Three systems that demoed well and needed engineering to survive production.
Challenge: The assistant answered confidently from the wrong document. After two bad answers in front of clinicians, nobody opened it again.
Solution: We rebuilt retrieval — chunking, hybrid search and re-ranking tuned on their corpus — added a golden test set, enforced citations and built a refusal path for low-confidence queries.
Challenge: The bot answered questions but every action needed a human. Deflection stalled at 20% and the support queue never moved.
Solution: We rebuilt it as a tool-using agent with scoped order-system access, approval gates on refunds above a threshold, and a full action audit trail.
Challenge: The monthly LLM bill tripled in a quarter and p95 latency sat at eight seconds. Every request hit the most expensive model available.
Solution: Model routing so simple requests use small models, plus semantic caching, prompt compression and streaming — validated against the existing evaluation set so quality held.
Get a free 30-minute architecture review. We'll look at your retrieval, evaluation and token spend, and tell you the three things worth fixing first — no pitch.
Rarely one model. We benchmark candidates on your task and your data, then route between them — a small model for the easy 80%, a large one for the rest.
Strong general reasoning and multimodal support — a dependable default for broad workloads.
Long-context and instruction-following strength — our usual pick for document-heavy RAG.
Natively multimodal with long context and tight Google Cloud integration.
Open weights, self-hostable — when data cannot leave your infrastructure at any price.
Efficient open models — often the right router target for high-volume, simple calls.
Built for retrieval workloads, with citation support as a first-class feature.
Multi-model access inside your own AWS account, with enterprise controls and residency.
Enterprise governance and regional data residency for regulated workloads.
A structured, outcome-driven path to designing, building and scaling LLM systems — with accuracy, cost and reliability treated as requirements, not hopes.
Define the use case, the accuracy bar it must clear, and the cost and latency it must live within.
Retrieval design, model selection and system architecture against those budgets — before code.
Chunking, embeddings, retrieval, re-ranking and prompts tuned on your actual content.
Build the golden test set and automated scoring first, so every later change is measurable.
Model routing, caching, compression and streaming to hit the cost and latency targets.
Safety filters, groundedness checks, refusal behaviour and red-teaming before launch.
Deploy into your stack with SSO, RBAC and a phased rollout with a fallback path.
Tracing, quality scoring, drift and cost dashboards — then iterate against the numbers.
Book a free, no-obligation architecture review. We'll assess your retrieval, evaluation, cost and guardrails, and give you a costed plan with a realistic timeline.
The depth production LLM systems actually need — retrieval, evaluation, agents, cost engineering, guardrails and observability.
Chunking, hybrid search, re-ranking and permission-aware retrieval that returns the right context, not just nearby text.
System design, structured outputs, prompt versioning and regression testing — prompts treated as code.
Golden test sets, automated scoring, groundedness and hallucination measurement, model bake-offs.
Tool use, planning, multi-step execution, approval gates and recovery from partial failure.
LoRA / QLoRA, instruction tuning and dataset curation — with honest advice on when not to bother.
Model routing, caching, compression, batching and streaming against explicit cost and latency budgets.
Safety filters, refusal design, PII redaction, bias testing and red-teaming before customers see it.
Tracing, quality monitoring, drift detection, cost dashboards and CI/CD for prompts and models.
APIs, middleware and event pipelines that put LLM output inside the tools your teams already use.
A modern LLM stack aligned to your accuracy, cost, latency and data-residency requirements — chosen per workload, not by habit.
LLM systems, agents and RAG applications designed to automate decisions, lift productivity and deliver measurable outcomes across industries.
LLM engineering is one part of what we do. Explore our wider AI and data capabilities.
Readiness assessment, use-case prioritisation and a costed roadmap.
Custom LLM apps, RAG systems, chatbots and AI agents.
Bespoke ML, vision and decision systems trained on your data.
Connect AI to your CRM, ERP and existing workflows.
Pipelines, warehousing and BI to power your AI.
Conversational assistants for support, sales and internal ops.
The reason most of our clients come back for the next system.
"ZTS successfully built a well-designed website. The communication and effectiveness were great."
"I have to thank both Sharmila and Sourav for all their timely help in developing a World Class Website they were always ready to guide and help me and their support is really appreciated"
"Thanks to Zebra’s expertise Shopify developer who built my Shopify store. The user interface for managing tools is clear and concise, and easily accessible from the control panel. Also, they seamlessly transfered my old site from WIX to Shopify. I highly recommend ..."
"Thanks to ZTS developers, highly experienced and skilled to develop web applications with API integrations. I was expecting user-friendly options for both web app development and UI/UX designs and they provided me with that. And, the most impressive thing I found is ..."
They delivered beyond expectations — our project saw real growth thanks to ZTS Infotech's passion and commitment
Ms. Shikha Bhatia shares her positive experience with Zebra Techies Solution, where she talks about how our Organic Social Media Management Services helped her brand achieve consistent engagement and authentic growth!
They went above and beyond to support us during challenging times. The ZTS Infotech team delivered with dedication, professionalism, and care — truly a partner we can count on!
They helped us turn hurdles into wins — Sam's e-commerce transformed thanks to ZTS's committed support
Outstanding collaboration and flawless execution — ZTS Infotech made the entire process smooth and truly result-driven!
A partner that treats accuracy, cost and reliability as engineering problems with owners — not as things you hope improve.
Every change is measured against a golden test set. If we can't show you the number moved, we don't ship it.
Model routing, caching and compression treated as first-class work — not something to worry about after the invoice.
Retrieval tuned on your content, citations enforced, and a refusal path when the system is not confident.
Observability, guardrails and failure paths designed in from the first sprint, not retrofitted before launch.
Permission-aware retrieval, PII handling, audit trails and data-retention policy defined before we connect anything.
Retrieval, models, integration, frontend and DevOps under one roof. Nothing lost between vendors.
Tell us what you've built and where it's falling short. We'll come back with an honest assessment, the fixes that matter most, and a transparent estimate — free.
The discipline of turning a language model into a reliable product. It covers architecture, retrieval design, prompt and context engineering, evaluation, guardrails, cost and latency optimisation, integration and production monitoring. Calling an API is the easy part; everything around it is the engineering.
The API gives you an answer. Engineering is what makes that answer correct on your data, consistent across thousands of calls, fast enough for a user to wait for, cheap enough to run at scale, safe enough to show a customer, and observable enough to debug at 2am. That gap is where most LLM projects fail.
Usually, yes. In our experience most hallucination is a retrieval problem, not a model problem — the right context never reached the model. We start with an audit of retrieval quality and evaluation, and in many cases fixing chunking, re-ranking and citation enforcement solves it without changing the model or rebuilding the app.
RAG in most cases. Fine-tuning teaches style, format and tone; it does not reliably teach facts, and it goes stale the moment your data changes. If the problem is "the model does not know our information", that is retrieval. If it is "the model does not sound like us or follow our format", that may be fine-tuning. We will tell you which honestly, including when the answer is neither.
With a golden test set built from real questions and verified answers, plus automated scoring for groundedness, correctness and format compliance. We build it before tuning starts, so improvements are provable and regressions get caught in CI rather than by a customer.
Typically a lot. The common wins are model routing so simple requests stop hitting your most expensive model, semantic caching for repeated queries, prompt and context compression, and batching. We validate every optimisation against your evaluation set so cost drops without quality following it.
It depends on the workload, and often the answer is more than one. We benchmark candidates on your actual task with your data, weighing accuracy, cost, latency and data residency — then usually route between a small model for easy calls and a larger one for hard ones. We are model-agnostic and resell nobody.
Yes. Options range from enterprise APIs with no-training guarantees and regional residency, through to fully self-hosted open models where data never leaves your infrastructure. We add permission-aware retrieval so users only ever see what they are entitled to, plus PII redaction and audit logging.
By giving it scoped tools rather than more instructions. We define the actions it may take, wire them to your systems with least-privilege credentials, add planning logic for multi-step tasks, put approval gates wherever a mistake costs money, and log every action for audit.
It depends on scope. An architecture and evaluation audit is a small fixed-price engagement and often pays for itself in token savings alone. Full builds are priced fixed-scope or as a monthly dedicated team. The initial architecture review call is free.
