Our AI development services bring production-grade AI to startups, SaaS, and enterprise teams. From LLM-powered features and autonomous agents to NLP pipelines and AI chatbots, you get senior engineers, eval frameworks, cost-optimized inference, and full ownership of the source code. Whether it's your first AI feature or a platform serving thousands of users, we build systems designed to perform in the real world.
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150+
Products Shipped
60+
Engineers In-House
96%
Client Retention
Built for teams at every stage of their AI journey.
Before you commit to a path, choose the right one.
Each path has genuine strengths; the right choice depends on your use case, volume, and goals. Here's how the three compare across the dimensions that matter most twelve months in. Zethic builds the first two; we'll tell you honestly when the third is the right call.
| What matters |
Recommended
Custom build
|
Common alternative
Off-the-shelf SaaS
|
Other path
Low-code platform
|
|---|---|---|---|
| Time to first value | 10 to 16 weeks to a production system built for you | Days to a working prototype, fastest path to validation | Hours to start, within the vendor's feature set |
| Staying current with frontier models | You control the upgrade cycle and timing | Automatic new releases reach you immediately | Vendor manages upgrades on their schedule |
| Fit to your domain | Built around your data and edge cases, highest accuracy ceiling | General-purpose, shaped by your prompts | Pre-built workflows, fastest to adopt as-is |
| Long-term cost (3 yrs) | Flat after launch, inference optimized for your volume | Pay-as-you-go scales smoothly at low to medium volume | Predictable per-seat pricing at smaller sizes |
| Maintenance | Full control, SLA-backed if you want | Minimal, provider handles model ops | Fully managed by the vendor |
| Data ownership | Your models, pipeline, embeddings | Governed by the provider's policies | Managed within the vendor's platform |
| Compliance posture | Your audit trail, controls, and governance | Inherits the provider's certifications | Inherits the platform's certifications |
| Flexibility over time | Codebase evolves with your business | Easy to switch providers | Tied to the vendor's roadmap |
Full-lifecycle AI development, from strategy to production systems. Pick the capability that matches your stage. Each comes with the same senior pod, the same eval discipline, and full code ownership at handover.
We audit your data, stack, and goals, then hand over a prioritized roadmap, a realistic cost model, and a build-vs-buy call for every use case.
Use-case auditData readinessBuild vs buyReference architecture
We audit your data, stack, and goals, then hand over a prioritized roadmap, a realistic cost model, and a build-vs-buy call for every use case.
Agents that plan, reason, and act across your systems, calling APIs, making decisions, and escalating to a human at the right moment.
Agentic AIMulti-agent pipelinesTool useHuman-in-the-loop
Agents that plan, reason, and act across your systems, calling APIs, making decisions, and escalating to a human at the right moment.
LLM features in your product: RAG, summarization, and domain assistants, each shipped with evals, prompt versioning, and a cost ceiling.
RAGFine-tuningPrompt versioningEval suites
LLM features in your product: RAG, summarization, and domain assistants, each shipped with evals, prompt versioning, and a cost ceiling.
AI capabilities added to your existing software without a rewrite, tested against your real data and built to degrade gracefully.
API integrationMiddlewareLegacy systemsFallback logic
AI capabilities added to your existing software without a rewrite, tested against your real data and built to degrade gracefully.
Systems that turn unstructured text into structured, usable data: classification, NER, sentiment, intent, and semantic search.
Text analyticsNERSentimentSemantic search
Systems that turn unstructured text into structured, usable data: classification, NER, sentiment, intent, and semantic search.
Multi-turn agents for support, sales, and helpdesk on web, mobile, WhatsApp, and Slack, with clean handoff to human agents.
Conversational AIWhatsAppSlackCRM-tied bots
You don't need to ship every AI capability on day one.Tell us where you are. We'll tell you what to build first.
What we've shipped, across AI categories.
Different AI shapes, same engineering discipline. Whatever the category, you get the same senior pod, the same eval cadence, and full ownership of models, pipelines, and code at handover.
Replace ten spreadsheets with one source of truth.
Retrieval-augmented assistants grounded in your own documents, wikis, and tickets, with citations and confidence thresholds built in.
Extraction, classification, and summarization over contracts, invoices, and forms, wired into your existing back-office systems.
Agents that coordinate across tools and APIs to run procurement, vendor, and operations workflows with humans in the loop.
Where money actually moves through your product.
Semantic search, recommendation, and matching engines that rank on meaning, not just keywords, across high-volume catalogs.
ML scoring models for fraud, credit, and risk, shipped with feature pipelines, drift monitoring, and explainability.
Multi-turn support, sales, and helpdesk bots on web, WhatsApp, and Slack, with clean handoff to human agents.
What your end users actually touch.
Live eval dashboards, golden datasets, and drift detection so quality is measured continuously, not guessed at.
Lightweight inference in mobile and edge apps, with offline fallback and cost-aware model routing.
AI for finance and healthcare with audit trails, PII redaction, and human-in-the-loop checkpoints where precision matters most.
AI for every vertical.
Our technology
A model-agnostic, production-tested toolkit chosen for accuracy, cost control, and long-term maintainability. OpenAI, Anthropic, open-source models, and your own fine-tunes, all owned by you at handover.
AI inside our delivery, not just in the product.
AI is a tool in our delivery pipeline as much as it is the thing we build for you. Here is how it changes the work, the speed, and the quality of what we hand over.
Every AI feature ships with a test harness, golden datasets, and live eval dashboards. No silent regressions when a model version flips or a prompt changes.
Senior engineers pair with Cursor and Copilot where it earns its keep, with a typed style guide and review gates so generated code clears the same bar as hand-written.
Token budgets, caching, batching, and right-sized model selection are designed in before launch, so inference cost scales with value rather than usage curves.
Faster use-case teardowns, data-readiness audits, and architecture options. You get more shape on the problem in the first two weeks, not the first two months.
Every line of AI-generated code is reviewed by a senior engineer and ships under your IP, the same as anything else we write. No model-generated code goes to production unattended.
The pod that scopes the work is the pod that ships it. No outsourcing, no junior teams, no account managers sitting between you and the engineers doing the build. Clients in the US, UK, Middle East, and Southeast Asia receive the same engineering quality they would expect from a top London or New York firm, at India-based pricing.
Eval framework, golden datasets, and acceptance criteria are defined before the first model call. Test harness and live eval dashboards catch regressions pre-release. Where precision matters most, we add human-in-the-loop checkpoints.
Token budgets, model selection, caching, and batching are designed from day one. We treat inference cost as a first-class engineering constraint, so cost scales with value, not with usage curves.
Your models, embeddings, pipelines, training data, and IP are yours to run, fork, and extend forever. Everything lives in your repo and cloud account. You stay free to switch providers, swap models, or take the system in-house at any time.
Selected work Real outcomes.
Production AI depends on strong systems engineering: clean data flows, audit trails, secure integrations, and workflows that hold up under compliance review. The same discipline behind 150+ products powers every AI system we ship.
View All Case StudiesOur achievements display our capabilities
Trusted voices. Real outcomes.
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
Four phases built for AI delivery. Working builds every two weeks. Evals from the start, accuracy thresholds agreed up front, inference costs designed into the architecture.
Book a scoping call{ 01 }· 1 to 2 weeks
Audit data, validate the use case, benchmark approaches, define measurable success. Output: reference architecture, model rationale, eval plan, cost band.
{ 02 }· 2-week sprints
Model development, prompt engineering or fine-tuning, API and UI integration, working demos every two weeks.
{ 03 }· Before go-live
Red-teaming, adversarial and load testing, bias audits, compliance review against real edge cases.
{ 04 }· Launch and ongoing
Staged rollout, real-time monitoring, drift detection, on-call handover, weekly reviews, SLA-backed.
Engagement models, built to fit.
Senior AI engineers in your workflow, shipping every sprint. Choose the contract shape that matches your stage, your budget, and how much in-house AI capability you already have.
A scoped AI build with a defined deliverable, timeline, and price. Best for first AI features, proofs of concept, and use cases with a clear definition of done.
A senior AI pod embedded in your tools and rituals, shipping every sprint. Best for teams rolling out AI across multiple use cases without a six-month hiring cycle.
The full process of building, integrating, and deploying AI systems: consulting, data pipelines, model development and fine-tuning, integration, and post-launch monitoring.
We start by mapping what you already have. Most projects need some extraction, cleaning, labeling, or enrichment, and we scope that work transparently up front, usually 15 to 30% of the budget. Discovery gives you a clear picture of what you have, what you need, and the fastest path between them.
Grounding and guardrails. RAG ties answers to your actual sources, eval suites verify quality before release, and every system ships with fallback logic and confidence thresholds. Where precision matters most, we add human-in-the-loop checkpoints.
We're model-agnostic by design. We work with OpenAI, Anthropic, open-source models like LLaMA and Mistral, and your own fine-tuned variants, choosing whichever meets your accuracy bar and budget. You stay free to switch as the market evolves.
We architect so models are swappable. Prompt versioning, an eval harness, and a clean model interface let us benchmark each new release against your golden datasets and adopt it when it improves your metrics, no rewrite required.
You do, fully: models, weights, embeddings, training data, and pipelines live in your repo and cloud account.
Eval frameworks, bias testing, adversarial red-teaming, drift monitoring; architected to SOC 2, ISO 27001, GDPR, DPDP, and HIPAA.
We treat inference cost as a first-class engineering constraint from day one: right-sized model selection, caching, batching, and token budgets are designed in before launch. As usage grows, we tune model routing and infrastructure so cost scales with value.
Cost transparency, before the call.
Most agencies will not put a number on the page. Here is the honest band by AI build type, plus the five things that actually move the number once we scope your specific use case.
| Tier comparison | Validating a use case AI proof of concept $15K - $50K ₹12L - ₹40L |
Most chosen
Shipping to real users Production AI feature $50K - $150K ₹40L - ₹1.2Cr | AI across multiple teams AI platform $150K - $500K ₹1.2Cr - ₹4Cr | Ongoing capacity Embedded AI pod from $20K / month from ₹16L / month |
|---|---|---|---|---|
| Timeline | 4 to 8 weeks | 10 to 16 weeks | 5 to 9 months | Sprint-by-sprint |
| What you get |
|
|
|
|
How much extraction, cleaning, labeling, and enrichment your data needs before a model can use it, usually 15 to 30% of the work.
The higher the precision requirement, the more eval work, fine-tuning, and human-in-the-loop checkpoints the system needs.
Every external system and data source (CRM, ERP, knowledge base, identity) adds discovery, contract testing, and failure handling.
SOC 2, HIPAA, RBI, DPDP, and similar add architecture constraints, audit work, PII handling, and pre-launch reviews.
Expected request volume drives model selection, caching, and infrastructure, the difference between a flat cost and a runaway one.
Bands include data work, engineering, evals, MLOps, and PM. Model and inference fees, hosting, and third-party licenses are billed at cost.
Tell us your stage, your data situation, and what you want to achieve. A senior engineer replies within one working day, ready to talk specifics.
Step 1 - Tell us where you are
First AI project, scaling an existing model, or building a platform. We sign an NDA before any specifics.
Step 2 - Speak to an engineer
A senior engineer joins within two working days to map goals, data readiness, and the shortest path to a working AI system.
Step 3 - Get a real plan
A reference architecture, a model-selection rationale, a cost band, and an engagement model tailored to your stage.
Tell us your stage, your data situation, and what you want to achieve. A senior engineer replies within one working day, ready to talk specifics.
