10decodersEnterprise RAG that holds up in audit
Knowledge assistants grounded in your policies, contracts and clinical documents, with citations back to the source passage.
NeMo RetrieverReranking NIM
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NVIDIA AI engineering
The NVIDIA stack is ready. Your architecture decisions are not.
We are the engineering team that takes NIM, NeMo and NVIDIA Blueprints from a working demo to a production system your security team signs off and your CFO can read the cost of.
Where we work in the stack
ApplicationsBlueprints, agents
Retrieval and guardrailsNeMo Retriever, Guardrails
InferenceNIM, TensorRT-LLM, Dynamo
OrchestrationKubernetes, GPU Operator
ComputeCloud GPU, RTX PRO, DGX
200+
Engineers
37+
Clients served
ISO
27001 and 9001
4
Global offices
Where teams get stuck
The hard part of NVIDIA AI starts after the demo works.
We hear some version of these in almost every first call.
01
The Blueprint ran in an afternoon. Nobody has scoped what production actually needs.
Auth, data pipelines, evaluation, monitoring and rollback are not in the notebook.
02
Our patient and customer data cannot leave the building.
Hosted LLM APIs are off the table, so the model has to run where the data already lives.
03
We have GPUs. We cannot tell you if they are busy or wasted.
Batching, quantisation and scheduling were never tuned for the real traffic pattern.
04
Every vendor quotes tokens. Nobody shows cost per resolved case.
The board wants a unit cost it understands before approving the next phase.
How we read the stack
Every layer has a decision that changes cost, latency or risk.
Knowing the component names is easy. Knowing which choice at each layer breaks your budget six months later is the job.
Layer
NVIDIA components we use
What we decide with you
ApplicationsBLUEPRINTS AND AGENTS
NVIDIA BlueprintsAI-QNeMo Agent ToolkitVideo search and summarization
Which parts of a Blueprint to keep, which to replace with your systems, and where a human must approve the agent's action.
RetrievalDATA TO CONTEXT
NeMo RetrieverEmbedding NIMReranking NIMGPU vector search
Chunking by document structure, whether re-ranking earns its latency, and how retrieval quality is measured before users see answers.
ModelsSELECT AND ADAPT
NemotronOpen models on NIMNeMo CustomizerNeMo Evaluator
The smallest model that passes your evaluation set, and whether fine-tuning beats better retrieval for your task.
SafetyGUARDRAILS AND AUDIT
NeMo GuardrailsSafety NIMsTrace logging
Where PHI and PII are masked, which topics are blocked, and what evidence your auditor gets for every answer.
InferenceSERVE AT SCALE
NIMTriton Inference ServerTensorRT-LLMDynamo
Quantisation level, batching and concurrency targets, and the break-even point against a hosted API.
ComputeWHERE IT RUNS
Cloud GPU instancesRTX PRO serversDGX systemsJetson edgeGPU Operator
Cloud, on-premise or edge, sized for GPU memory, context length and peak concurrency rather than a vendor's reference sheet.
What we build
Six things we ship on NVIDIA, each tied to a problem we have already solved.
Agentic workflows with a kill switch
Multi-step agents for claims, onboarding and research tasks, with approval gates, circuit breakers and full traces.
NeMo Agent ToolkitAI-Q
On-premise and sovereign inference
Models served inside your data centre or private cloud for HIPAA, DPDP and RBI-regulated workloads. No data leaves your perimeter.
NIMRTX PRODGX
Vision AI on existing cameras
Detection, tracking and video search on CCTV and aerial feeds, tuned for poor light and edge hardware. Productised in VuFindr.
MetropolisDeepStreamJetson
Document intelligence for healthcare and lending
Extraction from faxes, scans and handwritten forms with field-level confidence and human review routing. Productised in DocuFindr.
NeMo Retriever extractionVision language NIMs
Inference cost engineering
Benchmarking, quantisation and serving changes that cut cost per task, reported in a unit your finance team uses.
TensorRT-LLMDynamoTriton
Architecture review
Four questions we answer before anyone orders a GPU.
1
Self-host on NIM or call a hosted API?
We model both against your real volume, data residency rules and latency target. Spiky, low volume work often stays on an API.
2
What is the smallest model that does the job?
We build an evaluation set from your documents first, then test model sizes against it. Bigger is rarely the fix for a retrieval problem.
3
Which GPU, and how many?
Sizing comes from model memory, context length and peak concurrent users. We show the math so procurement can check it.
4
What does an answer cost, and who can prove it was right?
Every design ends with a cost per resolved task and an audit trail that links each answer to its source and its guardrail checks.
How to start
Start small. Scale only after the numbers hold.
Entry2 weeks
NVIDIA architecture review
Fixed fee
- Use case and data readiness check
- Reference architecture on your infrastructure
- API vs self-hosted TCO model, including licences
- Go, no-go or wait recommendation
If we conclude you should not build yet, we say so in writing.
Most teams start here6 to 8 weeks
Blueprint to production sprint
Fixed scope
- One use case live on your cloud or on-premise GPUs
- Evaluation set and quality gates agreed in week one
- Guardrails, tracing and cost dashboard
- Runbook and handover to your team
- Architecture review fee credited
If the quality gates agreed in week one are not met, the final phase is not billed.
ScaleOngoing
Dedicated NVIDIA engineering pod
Monthly
- ML, platform and data engineers from our GCC model
- New use cases on the same platform
- Model and NIM version upgrades
- Quarterly cost and quality review
30-day notice. No lock-in on your models or infrastructure.
Straight answer
Where we are not the right partner.
You are building a data centre.DGX SuperPOD procurement, power, cooling and networking belong with an NVIDIA Elite solution provider. We work on top of what they install.
You need a 300-person AI programme next quarter.Global integrators staff at that scale. We run focused pods that ship one system at a time.
You have no use case yet.Start with our GenAI workshop instead. Buying a stack before a problem is the most expensive mistake in this space.
From the R&D lab
This practice grew out of problems our lab was already working on.
Our R&D team tests models, retrieval designs and GPU serving on real healthcare and vision workloads before any of it reaches a client.
ManikandanCo-founder, Chief AI and Innovation Officer. Leads the NVIDIA engineering track.
01
NVIDIA Engineering LabNIM serving benchmarks, NeMo Retriever evaluation
02AI pipelines and models for business casesModel selection, retrieval architecture, cost at scale
03Experiments in visionDetection and tracking under poor conditions, edge inference
04OCR and semantic models for healthcare documentsDegraded documents, confidence models, human review
Engineering notes
What we have written about building on NVIDIA.
GenAI development on NVIDIA in 2026: the stack and which dev kit to buy
Blueprints, NIM, NeMo, Jetson, DGX Spark. Map NVIDIA's GenAI stack and compare developer kits on price and purpose before you buy hardware.
How to build a production RAG pipeline with NVIDIA NeMo Retriever and NIM
Multimodal extraction, embedding, hybrid search, reranking, guardrails and evaluation, step by step.
FAQ
Questions buyers ask before an NVIDIA build.
Is 10decoders an NVIDIA partner?
We are an AI engineering firm that designs and ships systems on the NVIDIA AI Enterprise stack.
Do we need NVIDIA AI Enterprise licences to use NIM?
NIM microservices can be used for development through the NVIDIA Developer Program. Production deployment is covered by an NVIDIA AI Enterprise subscription. We include the licence cost in the TCO model during the architecture review so it is not a surprise later.
Do we have to buy GPUs to work with you?
No. Most engagements start on cloud GPU instances from AWS, Azure or Google Cloud. We recommend on-premise hardware only when data residency, steady high volume or latency makes the numbers work.
When should we self-host models on NIM instead of calling a hosted LLM API?
Self-hosting usually makes sense when data cannot leave your environment, when request volume is steady enough to keep GPUs busy, or when you need predictable latency. For spiky or low volume workloads, a hosted API is often cheaper. We model both before recommending either.
How long does it take to move a Blueprint into production?
For one well scoped use case with accessible data, our sprint runs six to eight weeks. The main variables are data readiness, security review and integration with existing systems.
Architecture review
Bring us the demo that works. We will tell you what production takes.
A 30-minute call with an engineer, not a sales script. You leave with a view on API vs self-hosted, likely GPU sizing and the biggest risk in your plan.
R&D: manikandan@10decoders.com
Delivery: thomas@10decoders.com
NVIDIA, NIM, NeMo, Nemotron, TensorRT, Triton, DGX, RTX, Jetson, Metropolis and DeepStream are trademarks of NVIDIA Corporation. References describe the technologies 10decoders builds on and do not imply endorsement by NVIDIA.
