AI & Machine Learning · CORTEX
AI & Machine Learning.
Production-grade AI and machine learning, from generative AI and RAG to agentic systems and MLOps. Built by engineers who've shipped to enterprise scale, not by prompt-engineering bootcamps.
- Sub-services
- 8
- Department
- CORTEX
The problem
Most enterprise AI projects don’t ship.
The proof-of-concept graveyard is large. A typical enterprise has dozens of AI experiments, Jupyter notebooks, demo apps, internal hackathon outputs, that look impressive in a slide deck but never reach production. Why? Because shipping AI is software engineering, not prompt engineering. Production AI needs an evaluation harness, retrieval pipelines, governance scaffolding, observability, cost controls, retraining loops, and an on-call rotation. Most teams skip those steps and pay for it later.
Prosigns ships production AI from day one. We start with an AI Readiness Assessment, design for evaluation and governance before we write the first prompt, deploy on infrastructure your security team will sign off on, and operate the system after launch. Sixteen specialized internal departments, including CORTEX (AI/ML), FOUNDATION (data engineering), and CITADEL (security): work together so AI deployments don't stall at the integration layer. The bench you see in the proposal is the bench in production.
What we deliver
8 capabilities, one bench.
Generative AI, agents, computer vision, predictive analytics, and MLOps, engineered for production.
- 01
Generative AI & LLM
RAG architectures, fine-tuning, prompt engineering, enterprise deployment.
- 02
AI Agents & Automation
Multi-agent systems, tool use, orchestration, production monitoring.
- 03
Computer Vision
Detection, OCR, video analytics, medical imaging, edge deployment.
- 04
Predictive Analytics
Forecasting, recommendations, churn, anomaly detection.
- 05
Intelligent Automation
Document understanding, decisioning agents, process orchestration.
- 06
n8n Automation
Production n8n consulting, self-hosted at scale, LLM workflows.
- 07
MLOps & AI Infrastructure
Model CI/CD, monitoring, feature stores, governance.
- 08
AI Strategy & Consulting
Readiness assessments, roadmaps, build-vs-buy guidance.
How we engage
4 phases, in writing.
The methodology shows up in the statement of work, not as slogans, but as deliverables, owners, and acceptance criteria.
- 01
Diagnose
AI Readiness Assessment maps your data maturity, infrastructure, talent, governance, and use-case backlog. We surface what to build first, what's pre-mature, and what's already AI-shaped but unlabeled. Output: a prioritized roadmap and a defensible budget.
- 02
Design
System architecture before model selection. We design the retrieval pipeline, evaluation harness, governance controls, and cost ceilings before picking GPT, Claude, Gemini, or open weights. Models change every quarter; the architecture has to outlast them.
- 03
Deploy
Production-grade infrastructure on AWS, Azure, or GCP: with managed model endpoints, vector stores, observability, prompt versioning, and rollback. Security review and compliance evidence collection happen in parallel, not after launch.
- 04
Operate
Continuous evaluation against ground-truth datasets. Drift monitoring. Retraining cadences. Cost optimization. Quarterly model upgrades when better options ship. We run what we built. Or hand off to your team with the runbook.
Capabilities
What’s in scope.
- Generative AI integration (GPT, Claude, Gemini, Cohere, open-weight models)
- Retrieval-Augmented Generation (RAG): hybrid retrieval, reranking, citation tracking
- Fine-tuning and domain adaptation (SFT, LoRA, RLHF where appropriate)
- Production-grade agentic systems with tool use, function calling, and orchestration
- Computer vision: object detection, OCR, document understanding, video analytics
- Predictive analytics: forecasting, recommendation engines, churn, demand modeling
- MLOps: CI/CD for ML, model registries, feature stores, A/B testing, monitoring
- AI governance: data lineage, model cards, evaluation harnesses, audit trails
Stack
Tools we use in production.
- Foundation models
- OpenAI GPTAnthropic ClaudeGoogle GeminiCohereMistralLlama
- Frameworks
- LangChainLlamaIndexHaystackDSPyVercel AI SDK
- Vector & retrieval
- PineconeWeaviateQdrantpgvectorElasticsearch
- Cloud AI
- AWS BedrockAzure OpenAIVertex AISageMaker
- MLOps
- MLflowWeights & BiasesCometRayBentoML
- Evaluation
- PromptfooRagasDeepEvalOpenAI EvalsLangSmith
Selected work
Quantified outcomes, not adjectives.
- 01Financial Services
+37%
fraud catch rateReal-time fraud detection for a US regional bank.
Replaced a rules-based engine with a streaming ML pipeline on AWS. Reduced false positives 42% while raising true catches.
9 months
- 02Healthcare
$4.2M
annual labor savingsRAG-powered clinical decision support across 240+ clinics.
FHIR-aligned RAG over 12M clinical documents. SOC 2-aligned audit logs, ePHI encryption, citation tracking on every answer.
11 months
- 03Logistics
−12%
fuel spendAI dispatch optimization at fleet scale.
Combined predictive ETAs with reinforcement-learning-driven dispatch. Migrated batch jobs to event-driven workers.
6 months
Common questions
Asked before the first call.
- 01
What kinds of AI/ML projects does Prosigns build?
Production systems across six practice areas: generative AI and LLM integrations, AI agents and automation, computer vision, predictive analytics, MLOps and AI infrastructure, and AI strategy/consulting. We don't take on research-only work or pilot-only engagements, every project is scoped to ship into production with monitoring, governance, and an operations plan.
- 02
Do you build production RAG systems?
Yes. RAG is one of our most common engagement types. We design hybrid retrieval (dense + sparse + filters), evaluate reranking strategies on your real corpus, build citation tracking so every answer is auditable, and instrument retrieval quality monitoring. Production RAG is more than embedding a vector DB. It's an end-to-end pipeline with eval harnesses and continuous improvement.
- 03
Do you fine-tune models or just call APIs?
Both, depending on what fits the use case. Most enterprise AI work today is best served by frontier API models (GPT, Claude, Gemini) with strong RAG and prompt engineering. Fine-tuning is the right call for narrow domains, latency-sensitive applications, or when data sovereignty requires self-hosted models. We assess and recommend honestly. Fine-tuning is often the wrong answer.
- 04
Can you build production agentic systems?
Yes. Multi-agent architectures with tool use, function calling, and orchestration, deployed with safety constraints, human-in-the-loop checkpoints, and full audit trails. We've built agents that route customer service, automate complex workflows, and orchestrate multi-step research tasks. Production agents need careful evaluation infrastructure; we build that in from day one.
- 05
How do you handle LLM cost optimization?
Cost is an architectural concern. We design with model tiering (cheaper models for routine work, frontier models for hard cases), prompt caching, semantic caching of common queries, retrieval to reduce context length, and continuous monitoring of token spend per use case. Most clients see 60–80% cost reduction within three months of our engagement vs their initial implementation.
- 06
Do you handle MLOps and ongoing operations?
Yes: through our Managed Services engagement model. We can run the ML platform we built (or one you built) under a published SLA, with on-call coverage, model retraining cadences, cost-optimization sprints, and quarterly model upgrade cycles. Or we hand off to your team with comprehensive runbooks and a 90-day shadowing period.
- 07
How do you handle data privacy and AI governance?
Governance is engineered in, not bolted on. We support self-hosted and private-cloud deployments where data sovereignty requires it. We build data lineage, model cards, and evaluation harnesses into every project. Our compliance posture (SOC 2 Type II in flight, ISO 27001 aligned, HIPAA-ready, GDPR/CCPA/PIPEDA covered) means procurement and security teams can audit us in days.
- 08
What are typical AI engagement budgets and timelines?
Advisory and AI Readiness Assessments start under $50K. Production builds typically run $250K – $1M for a 3–6 month focused engagement. Multi-quarter enterprise deployments (with MLOps, governance, and managed services) range $1M – $5M+. We publish brackets honestly so visitors self-qualify before the first call.
Related practices
Often paired with AI & Machine Learning.
Talk to us
Bring an AI & machine learning problem. We’ll bring a senior engineer.
A senior engineer plus the CORTEX department lead joins the first call. No discovery gauntlet, no junior reps, no obligation.