LLM serving performance
Engine selection and tuning, continuous batching, parallelism, quantization, speculative decoding and prefill / decode disaggregation.
vLLMSGLangTensorRT-LLM
SPECIALIZED MODELS + HIGH-PERFORMANCE INFERENCE
Make every GPU count.
Make every model matter.
Domain-specific model training and post-training, deep inference optimization, and private AI deployment — engineered together for your data, GPUs, production workloads and cost targets.
INFERENCE CONTROL PLANEARCHITECTURE / CONCEPT
Enterprise trafficApplications · agents · model APIs
INBOUND◈ Workload-aware routerPOLICY / SLO / COSTModel selectionKV-aware schedulingQuality guardrails
01 / PREFILL
Parallel compute
02 / DECODE
Token generation
TRACEABLE MEASURABLE PORTABLE
BUILT AROUND YOUR PRODUCTION REALITY
On-premColocationPrivate cloudHybrid
01 / THE DIFFERENCE
Better models are not enough.
Better AI systems win.
Enterprises need more than generic model APIs: private domain data, carefully designed evaluations, specialized model behavior, reliable serving and efficient GPU systems must work as one.
We connect model adaptation to inference economics, so a quality improvement also survives real traffic, latency requirements and operational constraints.
See what we optimize ↗01Evidence before training.
Measure the real task; compare prompting, retrieval, SFT and RL before touching model weights.
02Your models. Your infrastructure.
Customer-controlled artifacts, portable open stacks and deployments from owned GPU fleets to private cloud.
03Quality × speed × cost.
Optimize task accuracy, P95 latency, SLA-compliant goodput and cost per successful task together.
02 / WHO WE HELP
Four ways to begin.
One connected AI stack.
Whether you already own GPUs, spend heavily on model APIs, need private production AI, or want to train a specialist model on your industry data — start where the biggest constraint sits.
GPU OWNERS & OPERATORSFrom GPU investment
From GPU investment
to productive inference.
Buying GPUs is only step one. We help plan placement, integrate clusters, deploy serving infrastructure, and extract more useful work from the hardware you already pay for.
- Data center / colocation readiness and technical coordination
- Cluster bring-up, Kubernetes, drivers, networking and monitoring
- vLLM / SGLang / TensorRT-LLM / Dynamo integration and tuning
- Workload benchmarking, capacity planning and lifecycle operations
API-INTENSIVE ENTERPRISESMake model spend
Make model spend
work harder.
Your model bill is a systems problem, not just a pricing problem. Measure end-to-end cost per successful task and route every request toward the right quality, speed, and price.
- Unified gateway across commercial and open-weight model providers
- Quality-aware model routing, fallback, caching and budget controls
- Per-workflow cost / latency / success-rate instrumentation
- Selective migration to optimized private inference endpoints
PRIVATE / REGULATED DEPLOYMENTSYour model.
Your model.
Your production environment.
Move from an AI proof of concept to a governed production system with clear operational ownership, access controls, release gates and observable service-level objectives.
- Private VPC / on-prem / hybrid deployment architecture
- Model serving with authentication, policy and observability
- Evaluation gates, canary rollouts and safe rollback paths
- Long-term reliability, cost attribution and incident readiness
INDUSTRY MODEL TRAINING & POST-TRAININGOwn specialized
Own specialized
intelligence.
Train or adapt open-weight models for your business taxonomy, workflows and data. We design the evaluation, data pipeline, post-training recipe and efficient deployment together.
- Domain-data audit, permissioning, curation and expert labeling
- Continued pretraining when justified; SFT / LoRA / preference optimization
- Reward design, reinforcement fine-tuning and model distillation
- Independent quality evaluation, checkpoint governance and production serving
03 / CORE ENGINEERING
Optimization is not
one switch.
Deep inference systems engineering stays at the center of our work. We now connect data and model post-training to kernel, serving, routing and enterprise integration decisions.
GPU cluster & deployment
Inference-ready cluster architecture, multi-node scheduling, networking, GPU placement, production rollout and operating procedures.
KubernetesMulti-GPUColocation
Routing & resource efficiency
KV-aware routing, queueing, autoscaling, admission control, model placement, heterogeneous hardware and fault isolation.
KV cacheDynamoSLO routing
Inference economics
GPU cost attribution, token-level analytics, request traces, model comparisons and capacity planning using real production traffic.
$/taskGoodputTTFT / ITL
Enterprise AI gateway
OpenAI-compatible access, multi-provider policies, observability, cost allocation, evaluation gates and application integration.
APIsGovernanceWorkflows
Domain data & evaluation
Data curation, domain labeling, golden datasets, adversarial scenarios, expert grading and reproducible task evaluations.
Data QASME EvalsTraces
Industry model post-training
Continued pretraining where appropriate, SFT/LoRA, DPO, reward design, GRPO/RFT and distillation — always with a held-out evaluation and production constraints.
PEFTDPO / RLDistill
Training-to-serving lifecycle
Checkpoints, adapter and experiment registries, reproducible releases, multi-LoRA serving, canary validation and feedback-driven iteration.
Model registryMulti-LoRACI / Eval
OUR PRINCIPLE We use proven open technologies where possible and custom engineering only where the workload justifies it.
04 / VERTICAL INTELLIGENCE
Train for the domain.
Engineer for production.
Your company's knowledge, decisions and workflows deserve a purpose-built model — but only when training beats a simpler alternative on real evaluations.
SPECIALIZED MODEL ENGINEERINGFrom proprietary data
From proprietary data
to deployable advantage.
We build a measurable development loop across domain experts, high-quality datasets, specialized model weights, inference hardware and live production signals.
Discuss a domain model01 / PREPAREEnterprise dataRights · labeling · splits
→
02 / DEFINETask evaluationGolden set · SME graders
03 / ADAPTSFT · DPO · RLLoRA · reward · distill
→
04 / SERVEOptimized inferenceQuality · latency · GPU cost
↶ PRODUCTION TRACES → EVALS → NEXT ITERATION
CHOOSE THE SMALLEST INTERVENTION THAT WORKS
Which adaptation strategy fits?
Explore trade-offs. Every route starts with a held-out benchmark and a production cost target.
01 / NO WEIGHT UPDATEEVALUATE FIRST
Start with retrieval before training.
Best when facts change frequently and must remain attributable to authoritative sources. Measure retrieval recall and answer faithfulness first.
BEST FIT
Policy knowledge, document-backed questions, fast-changing facts.
WATCH OUT
Retrieval quality, permissions, context cost and unsupported claims.
GATED BY → Grounded answer accuracy + cost per task
ILLUSTRATIVE WORKLOADS / NOT CLIENT CLAIMS
Financial documentsClassification · extraction · audit
Healthcare operationsAdministrative workflow support
Industrial systemsTechnical manuals · maintenance
Enterprise supportSpecialized agents · escalation
DELIVERABLES / EVALUATION-DRIVEN
Scope a model adaptation pilot ↗Weights alone are not a product.
Each scoped engagement can include data lineage, held-out evaluation sets, training recipes, checkpoints, inference integration, versioned rollout and an ongoing improvement plan. Data/model ownership follows the negotiated contract and underlying licenses.
05 / INFERENCE ECONOMICS
Measure the system.
Not just the tokens.
Explore how improving SLA-compliant output throughput could change GPU capacity economics. This is a transparent planning model — not a performance claim.
CAPACITY SCENARIOILLUSTRATIVE MODEL
WHAT CHANGES AT THE SAME QUALITY & SLA↗
Modeled cost / 1M accepted output tokens
$17.36→$13.35
Before · After assumed goodput upliftPotential capacity equivalent freed7.4 GPUs
Illustrative avoidable spend / month*$13,477
*Avoided spend assumes you can release or avoid buying equivalent GPU capacity at constant demand. Goodput means accepted output tokens delivered within your specified latency / quality thresholds. Excludes network, storage, idle overhead, engineering and reserved-capacity constraints. Validate on your actual workload before making investment decisions.
TTFT / P95ITL / P95SLA-COMPLIANT GOODPUTGPU-HOURSCOST / SUCCESSFUL TASK
06 / ENGINEERED END TO END
A system-wide view.
One accountable partner.
We connect data pipelines and model checkpoints to clusters, inference runtimes, routers and enterprise workflows — with independent evaluation and production rollback.
LAYER 01
→▥
Data & models
Domain data / evals
SFT / RL / registry
LAYER 02
→▦
GPU & serving
Clusters / engines
kernels / KV cache
LAYER 03
→⇄
Control plane
Scheduling / routing
SLO / observability
LAYER 04
◫
Business workflows
Gateway / tools / agents
domain evaluation
DEPLOYMENT FLEXIBILITYCustomer data centerColocation partnerPublic / private cloudHybrid
07 / HOW WE ENGAGE
Evidence first.
Engineering second. Outcomes always.
No opaque performance promises. Every engagement starts with workload evidence, explicit success criteria and a reproducible baseline.
STEP 01 / DISCOVER
01
Map the workload
Map tasks, domain data, existing models, traffic, infrastructure, data rights, regulatory constraints and business KPIs.
OUTPUT → Problem definitionSTEP 02 / BASELINE
02
Benchmark the bottleneck
Establish independent quality evaluations and representative latency/cost baselines before training or optimization.
OUTPUT → Measured opportunitySTEP 03 / OPTIMIZE
03
Build and deploy
Run a controlled training or inference optimization pilot, with model registry, acceptance criteria and rollout gates.
OUTPUT → Production-ready changesSTEP 04 / EVOLVE
04
Operate and improve
Monitor quality, drift, security, speed and unit economics; retrain or retune only when evidence supports it.
OUTPUT → Continuous optimizationA PRACTICAL FIRST STEP
Request an assessment ↗Start with an AI Efficiency & Model Fit Assessment.
A bounded baseline spanning model task quality, data readiness, inference economics and deployment constraints. Identify whether RAG, post-training or systems optimization creates the best return.
08 / COMMON QUESTIONS
Built for serious
technical decisions.
Questions you should ask any inference infrastructure partner.
Are you a GPU cloud or an inference platform?+
Our proposed primary offering is specialist engineering and optimization for your inference workloads, rather than a commodity GPU rental marketplace. We can work with your owned hardware, preferred cloud provider, or colocation infrastructure. Managed offerings may be added as customer demand is validated.
Do we need to replace our current inference stack?+
Usually not. We start by measuring the behavior of your existing stack. Where practical we tune and extend open tools such as vLLM, SGLang, TensorRT-LLM and NVIDIA Dynamo instead of imposing unnecessary migration.
Can you guarantee a specific speedup or cost reduction?+
No responsible engineering team should promise a universal speedup without representative workload tests. We establish a baseline, target explicit service-level objectives and evaluate changes against agreed acceptance criteria. Results vary by model, hardware and traffic.
Do you support private and regulated workloads?+
We can scope deployment architectures with data isolation, logging boundaries and customer-controlled environments. Specific certifications, compliance commitments and security assurances must be agreed contractually; none are implied by this demonstration website.
Should we use RAG, fine-tuning, or a different model?+
That depends on the task. We recommend beginning with an evaluation set and the simplest baseline that meets quality and operational goals, then comparing retrieval, prompting, fine-tuning, distillation and model routing as measurable alternatives.
How is a first engagement structured?+
Typically: workload discovery, baseline measurement, a written optimization hypothesis, a scoped pilot and acceptance criteria. We can then propose deployment engineering, advisory services or recurring optimization support.
Do you train foundation models from scratch?+
Not as our standard offering. Training a frontier foundation model from scratch is capital-intensive and rarely justified for enterprise specialization. We focus on open-weight model adaptation: selectively continued pretraining, supervised fine-tuning, preference optimization, reinforcement fine-tuning, distillation and the corresponding data/evaluation engineering.
How do you decide between SFT, DPO, RL and distillation?+
We establish independent evaluation data first. SFT helps teach demonstrations or formats; preference optimization helps shape preferred responses; reinforcement fine-tuning is attractive when reward signals reliably assess verifiable outcomes; distillation helps move stable capability into cheaper models. Retrieval remains preferable for changing factual knowledge. Training is not automatically the answer.
Who owns the fine-tuned weights and enterprise data?+
Customer control, permitted use, deliverable ownership, licenses and deletion obligations are documented in the contract and system design. Rights in fine-tuned artifacts also depend on the license of the base model and training data; a blanket promise of unrestricted ownership would be misleading.
09 / START A CONVERSATION
Have a real workload?
Let's make it work harder.
Share a real GPU bottleneck, model API bill, private deployment need or industry model training objective. We begin with a measurable evaluation, not a generic platform pitch.
↗ Technical discovery↗ Infrastructure evaluation↗ Workload benchmarking
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