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Last updated on: 2025-10-29

AI/ML Development & Support
— AI/ML Engineers Developers Solutions Company in India

Quick Answer

AI/ML Services for modern and legacy systems: LLM & RAG integration, embeddings & vector search, serving/inference (FastAPI/vLLM/ONNX), MLOps/DataOps pipelines, evaluations & guardrails, and long-term maintenance. We handle audits, migrations, and production support with disciplined DevOps and 24×7 coverage.

  • Custom LLM apps & APIs (REST/GraphQL)
  • RAG pipelines & vector databases
  • Serving/inference & scaling
  • MLOps, security & CI/CD pipelines

Additional Quick Answer

PrecisionTech brings ~30 years of engineering delivery. We offer end-to-end AI/ML development, takeover of existing codebases, and structured SLAs across India and globally. Start with a 6-hour support block or engage a dedicated pod.

  • Senior AI/ML engineers & architects
  • LLM/RAG, embeddings, vector search
  • Secure integrations & guardrails
  • Managed monitoring & model evals

Senior AI/ML engineers serving India and global teams with RAG, vector search, serving, MLOps, and SLAs.

Trusted by businesses across India and worldwide. We don’t just prototype; we stabilize, modernize, and scale your AI stack—LLM/RAG services, data workflows, and mission-critical integrations (payments, WhatsApp, Maps/GPS, ERP). Backed by ~30 years and thousands of delivered projects.

How to Engage Our AI/ML Team?

Start with a 6-hour support block for urgent fixes or small features, or book a discovery sprint remote or on-site. We’ll review your repo, data flows, and priorities, then propose a clear, low-risk plan.

Hire Expert AI/ML Engineers & Developers Team Hire Senior AI/ML Engineers & Developers Team

PrecisionTech delivers end-to-end AI/ML engineering globally: LLM & RAG (LangChain/LlamaIndex), embeddings & vector DBs (pgvector, Milvus, Elastic/OpenSearch), serving/inference (FastAPI, vLLM, ONNX Runtime), and MLOps (MLflow, DVC, Airflow/Prefect). We handle evaluations, performance tuning, and CI/CD for safe, repeatable releases.

We integrate the platforms your business relies on—payments, messaging, maps/GPS, ERP/CRMs—and bridge them with robust AI services and auditable logs. Engagements start with a quick 6-hour AI/ML support block (₹9,900), then scale to retainers or dedicated pods. Every project includes documentation, handover, and SLAs—so your AI stack stays maintainable, secure, and fast.

Our remit spans everything related to AI/ML: one-time hiring for a small fix, sprint-based features, large programs, and full product engineering. Whether you need a AI/ML development company, dedicated AI engineer, or a senior team to rescue/modernize your stack, we bring rigorous engineering, transparent communication, and long-term support.

Buy 6 Hours AI/ML Support — ₹9,900

Compare AI/ML service tiers to choose the best fit for your workload and release cadence.

Package Essentials Standard Advanced Enterprise
One-time Support Block
(Starter engagement) 		Try 6 hours block
Scoped fixes & small features 		— — —
Monthly Retainer
(Ongoing work) 		— 40–80 hrs / mo 80–160 hrs / mo 160+ hrs / mo
LLM & RAG integration (LangChain/LlamaIndex) Basic ✔ ✔ ✔
Embeddings & vector search (pgvector/Milvus) Baseline Enhanced Advanced Advanced+
Serving & inference (FastAPI/vLLM/ONNX Runtime) — ✔ ✔ ✔
MLOps & DataOps (MLflow, DVC, Airflow/Prefect) Basic Enhanced Advanced Advanced
Security & guardrails (PII policies, prompts, evals) Baseline Enhanced Advanced Advanced+
Monitoring & evaluations (telemetry, A/B, drift) Basic ✔ ✔ ✔
AI/ML
LLM
RAG
LangChain
LlamaIndex
pgvector
Milvus
FastAPI
vLLM
ONNX
MLflow
DVC
Airflow
OpenSearch
Grafana
AI/ML LLM RAG LangChain LlamaIndex pgvector Milvus FastAPI vLLM ONNX MLflow DVC Airflow OpenSearch Grafana

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Frequently Asked Questions

What AI/ML services do you provide?
Everything end-to-end: LLM & RAG integration, embeddings & vector search, conversational apps/agents, document Q&A, NLP/CV pipelines, model serving/inference, evaluations & guardrails, data/feature engineering, MLOps/DataOps, observability, migrations, and long-term maintenance.
Why choose PRECISION for AI/ML work?
30 years of production engineering. We pair solid architecture with measurable evaluations, CI/CD for models & prompts, sensible cost controls, and SLAs. We’re comfortable inheriting complex, messy stacks and making them stable, fast, and secure—without disrupting the business.
Do you support both hosted AI APIs and open-source models?
Yes. OpenAI/Azure OpenAI, Anthropic, Google, and open-source (Llama/Mistral/Whisper, etc.) via frameworks like LangChain/LlamaIndex. We deploy locally (vLLM/Transformers/ONNX Runtime/Triton) when data-residency, latency, or cost demands it.
Do you offer a prepaid AI/ML support block?
Yes. A convenient starter is 6 hours of AI/ML engineering support for ₹9,900. It covers quick fixes, feasibility checks, RAG prototypes, vector DB setup, prompt/eval tuning, and architecture consults. You can stack blocks or move to a retainer or a dedicated team.
What engagement models do you offer?
Fixed-scope projects, time-and-materials (hourly/blocks), monthly retainers, and dedicated engineer pods. We align with your release cadence, compliance, and budget.
How do you onboard an existing AI/ML codebase?
A fast health check: repo & infra review, model/provider inventory, data sources & privacy posture, vector store layout, prompt & eval baselines, latency/cost/perf metrics, and a prioritized remediation roadmap with low-risk increments.
Do you provide on-site AI/ML engineers?
Remote-first worldwide with optional on-site for discovery, launches, and eval/guardrail workshops. We can embed an engineer on premises for short sprints.
Can you take over an existing AI/ML project from another vendor?
Yes. We routinely inherit projects. We stabilize production first (cost/latency, evals, prompt safety, data/PII), then modernize safely without breaking users.
Which AI/ML stacks and frameworks do you support?
LangChain/LlamaIndex, Transformers, vLLM, ONNX Runtime/Triton, FastAPI serving, vector DBs (pgvector, Milvus, OpenSearch/Elastic), MLflow/DVC, Airflow/Prefect, and standard data tooling (Pandas/Polars, Spark where needed).
Do you build REST/GraphQL APIs for AI features?
Yes. Versioned endpoints with pagination/filtering, auth (JWT/OAuth2), rate limiting, idempotency, signed webhooks, request tracing, and full audit logs/metrics.
Can you integrate AI with payments, email, maps, and third-party APIs?
Yes. We frequently integrate payments (Razorpay/PayU/Stripe), WhatsApp Business API, Gmail/Calendar, Maps & GPS, ERPs/CRMs, and broker/data APIs—tying them into LLM workflows with robust logging and backoff/retry logic.
How do you approach AI performance, cost, and latency at scale?
Batching, caching (prompt/embedding), tool use minimization, context compression, retrieval filters, async pipelines, scalable serving (vLLM/ONNX), and continuous evaluation dashboards to balance quality/cost/latency.
Do you handle vector databases and RAG?
Yes. We design chunking/metadata strategies, hybrid ranking (BM25+embeddings), freshness policies, per-tenant isolation, and retrieval evaluators. We support pgvector, Milvus, and OpenSearch-based k-NN setups.
Do you support GPUs and on-prem inference?
Yes. NVIDIA GPU nodes (on-prem or cloud), containerized inference, autoscaling, model quantization, and fallbacks to CPU/hosted APIs for resilience.
Which servers, operating systems, and clouds do you support?
OS: AlmaLinux/Rocky, Ubuntu, Debian, required Windows integrations. Clouds: AWS/GCP/Azure and on-prem. Reverse proxies (Nginx/HAProxy), Docker/Podman, and Kubernetes when it truly adds value.
Do you work with orchestration and feature stores?
Yes. Airflow/Prefect for orchestration, MLflow/DVC for experiment/artifact tracking, Feast/feature-store-like patterns when useful, with proper lineage and governance.
How do you secure AI applications and protect data?
PII minimization/redaction, prompt-injection defenses, output validation, content filters, secrets management, TLS, RBAC, least-privilege, encrypted stores, and isolated eval sandboxes. We also add human-in-the-loop for sensitive actions.
Do you help with compliance (DPDP, GDPR, PCI, HIPAA-like)?
Yes. Purpose limitation & consent logs, data retention policies, anonymization/pseudonymization for non-prod, access auditing, DPIA-style checklists, and documented controls for audits.
How do you handle email deliverability for AI-driven notifications?
SMTP over SMTPS (465) with correct SPF/DKIM/DMARC alignment, bounce/complaint handling at volume, and inbox tests with Gmail/Outlook to confirm pass results.
Can you migrate between model providers or move on-prem?
Yes. We abstract providers, validate quality/economics with A/B and offline evals, then cut over with fallbacks and rollback points. We also move hosted→self-hosted (or vice-versa) when business needs change.
Do you refactor legacy prototypes into production systems?
We stabilize first (logging, retries, evals, guardrails), separate concerns (retrieval, orchestration, serving), and add CI/CD, tests, and dashboards—only splitting into services when it clearly reduces risk and unlocks velocity.
Do you write tests and set evaluation baselines for AI?
Yes. Deterministic unit/contract tests around pre/post-processing, golden-set evaluations for RAG, regression checks on prompts, and canary runs—wired into CI to prevent quality drift.
What observability do you set up for AI systems?
Structured logs (JSON/correlation IDs), token/cost/latency metrics, eval dashboards, drift monitors, and alerting. We use OpenTelemetry/Prometheus/Grafana/ELK as appropriate.
Who owns the code, prompts, and artifacts you produce?
You do—prompts, RAG pipelines, infra as code, dashboards, and automation live in your repos/accounts. Our reusable internal libraries remain ours; we respect OSS licenses and model terms.
Can you work under NDA and with our Git provider?
Yes. NDA is standard. We use GitHub/GitLab/Bitbucket with protected branches, PR reviews, required checks, and signed commits if needed.
What response times do you offer for AI incidents?
Business-hours response with emergency channels; faster SLAs are available on retainers. We work in IST and can extend overlap for global teams.
Can you start a discovery workshop this week?
Often yes. We can kick off remotely and schedule on-site sessions for stakeholders, product, and engineering.
Our AI system is failing (bad answers, high cost/latency). Can you help urgently?
Yes. Emergency stabilization: log capture, eval baselines, prompt/guardrail fixes, retrieval/index tuning, cache strategy, and a short remediation plan to prevent repeats.

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