Core practices

Engineering practices built for production AI

Direct senior engagement on architecture, model fine-tuning, and infrastructure from day one. No junior handoffs.

PHASE 01
01

GenAI architecture

Enterprise retrieval-augmented generation (RAG), fine-tuned LLMs, and multi-agent systems designed for low latency and zero data leakage.

Key deliverables
Hybrid vector search & embedding pipelines
Custom context window caching & token routing
Deterministic guardrails & evaluation benchmarks
Latency reduction
sub-50ms TTFT
Scope details
PHASE 02
02

MLOps infrastructure

Resilient inference clusters, continuous model monitoring, automated retraining pipelines, and GPU orchestration across multi-cloud environments.

Key deliverables
Kubernetes GPU autoscaling & spot instance routing
Drift detection & automated canary deployments
End-to-end model registry with lineage tracking
Compute cost efficiency
Up to 84% saved
Scope details
PHASE 03
03

Custom neural models

Bespoke deep learning architectures, domain-adapted foundation models, and quantized edge networks tailored to proprietary enterprise datasets.

Key deliverables
Domain-specific LoRA & full-parameter tuning
Model quantization (AWQ / GPTQ) for edge engines
Adversarial robustness & bias verification
Benchmark accuracy
99.2% precision
Scope details
PHASE 04
04

Data strategy & pipelines

High-throughput streaming pipelines, synthetic data augmentation, feature stores, and strict enterprise governance frameworks.

Key deliverables
Real-time streaming feature stores via Feast
Automated data synthesis & label verification
Zero-trust lineage & compliance boundaries
Ingestion throughput
12M events/sec
Scope details
SYSTEM_INTEGRATION_LIFECYCLE

How our squad embeds with your team

Direct collaboration with a 12-person senior machine learning collective. No junior handoffs, from initial architecture audit to live production telemetry.

PHASE 01
Weeks 1–2

Technical audit & architecture review

Full examination of your current data pipelines, model weights, compute footprint, and security constraints.

Core Deliverables
  • Inference latency bottleneck report
  • Model quantization feasibility audit
  • Architecture blueprint & cost model
Dedicated Squad: 2 Principal Researchers, 1 Systems Architect
SOC2 & ISO/IEC 27001 data isolation verification
PHASE 02
Weeks 3–6

Neural infrastructure & model prototyping

Rapid evaluation of model architectures, distributed training setups, and target fine-tuning benchmarks.

Core Deliverables
  • Custom LoRA & full fine-tuning harness
  • Synthetic data generation pipeline
  • Baseline evaluation with precision curves
Dedicated Squad: 2 Senior ML Engineers, 1 Data Strategist
Deterministic seed tracking & data lineage validation
PHASE 03
Weeks 7–10

Distributed training & squad integration

Pairing directly with your engineering leads to train models at scale and integrate inference endpoints.

Core Deliverables
  • Multi-GPU distributed training pipeline
  • Streaming RPC inference endpoints
  • Internal developer SDK & test suites
Dedicated Squad: 3 Senior ML Engineers, 1 MLOps Specialist
Differential privacy checks & red-teaming validation
PHASE 04
Weeks 11–12+

Production deployment & telemetry governance

Rolling zero-downtime deployment, continuous drift detection, and live automated fallbacks.

Core Deliverables
  • Sub-50ms edge caching & inference cluster
  • Real-time token drift & hallucination monitoring
  • Complete runbooks & client squad handoff
Dedicated Squad: 2 MLOps Engineers, 1 Principal Researcher
99.9% uptime SLA with real-time audit logs
TRANSPARENT_EXECUTION

Embedded engineering cadence and live SLAs

Bi-weekly sprint reviews

Direct live demos with senior engineers, zero account managers.

Dedicated engineering channel

Direct Slack or Teams link to the 12 engineers building your system.

Real-time telemetry access

Shared Grafana dashboards tracking throughput, loss, and latency.

Direct Senior Eng Engagement

Stress-test your inference and pipeline architecture

Schedule a focused 60-minute technical working session with our senior engineers. We dissect latency hot spots, audit model routing, and calibrate compute budgets with zero sales pitches.

< 45ms P99
Latency & Throughput Profiling
Pinpoint token generation bottlenecks, KV-cache memory saturation, and cluster routing delays under peak traffic load.
30-60% Cost Delta
Compute & Cost Optimization
Audit GPU compute allocation, quantization trade-offs, and batching strategies to reduce cloud inference expenditure.
Zero Hallucination Drift
Routing & RAG Topology Audit
Examine embedding retrieval density, vector database indexing efficiency, and fallback routing across foundation models.

Strict mutual NDA signed before session kickoff

Direct execution by principal AI researchers and MLOps staff

Actionable 10-page architecture teardown delivered within 48 hours