Custom LLM Development
End-to-end builds covering data prep, embedding strategy, retrieval, prompts, evals, and deployment, scoped against business outcomes.
- Architecture spec
- Evaluation harness
- Production deploy
Specialist engineers for custom LLM development, RAG pipelines, fine-tuning, evaluations, and LLMOps. Onboarded inside your VPC, on your stack, on your sprint cadence, from day one.
Trusted by enterprises across Retail, Manufacturing, BFSI, Logistics, and FMCG
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With 24+ years of enterprise delivery and a bench of 500+ elite engineers, orangemantra operates as a full-cycle LLM partner that builds secure, scalable language model systems backed by production-grade engineering and compliance practices.
Modern enterprises sit on data that general-purpose LLMs cannot synthesize. Hire LLM engineers who architect unified data layers, fine-tune domain models, and wrap every release in evaluations, guardrails, and observability. With generative AI development as the wrapper, custom LLM development becomes a measurable engineering programme.
Our Core LLM Capabilities
Every engagement moves through these three stages. Hire LLM developers who own each layer end-to-end, not specialists who hand off in the middle.
Fragmented enterprise data converted into clean, structured, machine-readable formats ready for embedding, indexing, and downstream training.
Base models such as Llama, Mistral, and Qwen adapted with PEFT and LoRA techniques, trained on internal jargon, policy, and process logic.
Automated guardrails embedded directly in the inference pipeline, with real-time evaluation suites catching model drift before it impacts production.
Pre-vetted LLM developers ready to start inside a fortnight. The bench covers retrieval, fine-tuning, agents, and evals without recruitment lag.
Engineers ship behind evaluation harnesses, not vibes. Every prompt change, retrieval tweak, and fine-tune is measured before it reaches production traffic.
Comfortable across OpenAI, Anthropic, Gemini, Llama, and Mistral. The right model for the workload, not the loudest brand.
Working RAG prototypes inside two to four weeks, then a hardened path to scale with guardrails, observability, and cost controls.
Hire LLM engineers who plan around data maturity, compliance posture, and procurement cycles, not a templated AI playbook.
If something breaks at 2 am, the LLM developers for hire are a Slack ping away. Coverage windows are set on the engagement, not on a generic SLA card.
The right answer depends on traffic shape, data residency, and how much explainability the business can defend. Hire LLM developers who frame the trade-off before they write code.
Best when call volume is modest and time-to-value matters. Engineers wire OpenAI or Anthropic behind a hardened retrieval layer with cost guards and rate-aware caching.
Llama, Mistral, or Qwen served on your cloud, behind your VPC, with quantisation and inference batching tuned to the SLA the business actually needs.
For sustained workloads with strict format, tone, or compliance requirements. Includes a reproducible training pipeline and a regression harness.
Specialist agents collaborating across tools, with deterministic orchestration where stakes are high. Pairs naturally with agentic AI development patterns for long-running tasks.
Sensitive workloads on-prem, scale-out workloads on managed inference. One control plane, one observability stack, one cost dashboard.
Short, sharp engagements to audit existing prompt apps, surface hallucination risk, and produce a remediation plan you can act on next sprint.
Hire LLM developers who build for the line items finance can verify: ticket deflection, search uplift, document throughput, fraud signal, and cycle time on knowledge work.
Explore your LLM use caseAI's impact on business is undeniable and immeasurable. Gear up with the orangemantra LLM engineering team.
The hiring path is built around enterprise procurement reality, not freelancer marketplaces. NDA on day one, profiles inside 48 hours, interviews on your schedule, and onboarding through your security stack.
Start the Hiring BriefA 30-minute call to map use case, data sources, compliance constraints, and the shape of the team needed: full-stack LLM, fine-tuning lead, agents specialist, or evals owner.
Three to five vetted LLM developers, ranked against the brief with prior work samples, evaluation portfolios, and rate cards. No bait-and-switch profiles.
Technical interview on your terms, optional paid trial sprint, and reference checks. Replace any engineer at no extra cost inside the trial window.
Engineers onboard to your identity provider, repos, ticketing, and data perimeter. Delivery cadence locks to your sprint rhythm from week one.
LLM economics shift by sector. The team scopes the build to where the document load, ticket load, or compliance load is already heaviest.
Clinical summarisation, EMR navigation, and prior-auth drafting under HIPAA-aware guardrails and audit logging.
KYC review, suspicious activity narratives, and policy-aware customer assistants under model risk management.
Catalogue enrichment, conversational search, and merchandiser copilots for catalogues that turn over fast.
Maintenance manual Q&A, supplier document parsing, and quality non-conformance summaries tied to ERP records.
Shipment status agents, exception triage, and contract clause extraction across 3PL and carrier networks.
Adaptive tutors, assessment generation, and curriculum mapping with answer-quality evals before any learner sees the model.
A working LLM system is a stack, not a single model. Hire LLM developers fluent across orchestration, vector stores, evaluation, and observability layers.
OpenAI GPT
Anthropic Claude
Google Gemini
Meta Llama
Hugging Face
Python / FastAPI
Node.js
pgvector
Elasticsearch
Redis
AWS Bedrock / SageMaker
Azure AI Foundry
Vertex AI
MLflow
Kubernetes
DockerThree models, one delivery floor. Switch between them as the build moves from pilot to scale, without re-signing a master agreement.
The first sprint usually delivers a working retrieval prototype. The next two harden it: evals, guardrails, observability, and cost controls before traffic moves over.
Talk to Our TeamReal reviews from teams that have shipped with orangemantra. Verified on Clutch and GoodFirms.
"The team treated evals as a first-class deliverable, not an afterthought. That alone made the rollout defensible."
Mar 2025
Feedback SummaryOrangemantra LLM engineers built a retrieval-grounded internal assistant across policy, HR, and IT documentation. The team handled data plumbing, embedding strategy, guardrails, and a full evaluation harness inside the project window.
"They cut our deflection-to-human ratio in half. Honest engineers who pushed back when our retrieval design was the bottleneck."
Sep 2025
Feedback SummaryA four-engineer pod built a grounded support assistant for a B2B SaaS product, including hybrid search over a 90k-article knowledge base, a fine-tuned reranker, and offline eval suites tied to escalation rates.
"Onboarded inside our VPC on day one. We never had to compromise on PHI handling to get the system shipped."
May 2025
Feedback SummaryOrangemantra delivered a domain-adapted clinical summarisation pipeline tied into the EMR, including PHI redaction, citation-grounded answers, and a review queue for clinical staff. Trained on internal protocol documents with reproducible runs.
"They cut our per-call inference cost by a meaningful margin without breaking latency targets. Real engineering work, not vendor theatre."
Aug 2025
Feedback SummaryThe engagement built a multi-model routing layer, caching, batching, and a regression harness for a high-volume FinTech use case. Production cost dashboards and red-team suites delivered as part of the handover.
Independent recognition from industry bodies and analyst platforms. Listed only where verifiable.
CIO Choice Recognition
Top IT Service
WARC Award
Globus Certifications
NASSCOM
ISO 27001An LLM engineer ships large language model systems into production: prompt design, retrieval-augmented generation, fine-tuning, evaluation suites, guardrails, and inference optimisation. The role focuses on the production behaviour of language models, not classical ML or computer vision.
A focused RAG pilot sits in the lower tens of thousands of dollars, a fine-tuned domain model with evals sits higher, and ongoing LLM consulting services bill by sprint. Orangemantra shares a fitted estimate after a scoping call.
Use a frontier API when latency tolerance is loose and per-call cost is acceptable. Fine-tune an open-source LLM when call volume is sustained, data residency matters, or the behaviour you need cannot be reached reliably with prompts alone.
Most engagements move from first call to billable work inside five to ten business days. Profiles arrive within 48 hours of the brief, interviews run on your schedule, and onboarding happens inside your VPC.
Yes. Orangemantra offers full-time dedicated LLM developers, part-time engagements for milestone work, and hourly rotations for spike workloads. The same bench covers generative AI developers and machine learning developers.
OpenAI, Anthropic, Google Gemini, Meta Llama, and Mistral, orchestrated through LangChain and LlamaIndex, with vector stores such as Pinecone, Weaviate, and pgvector. LLMOps runs on MLflow, managed platforms like Vertex AI and SageMaker, and observability via Langfuse or Arize.