Capabilities
What we build, end to end.
LLM & Agentic Systems
Multi-agent orchestration, RAG architectures, guardrails, and production-grade LLM deployment.
Data Engineering
ETL/ELT pipelines, data lake architecture, real-time analytics, and BI dashboards.
Cloud Architecture
AWS, GCP, Azure infrastructure. Sovereign cloud deployment for regulated industries.
Privacy & Compliance
PII detection, data classification, GDPR/PDPL governance platforms built from day one.
Voice AI & Conversational Agents
Inbound call handling, appointment scheduling, lead qualification, and customer service automation.
Intelligent Document Processing
Contract analysis, lease extraction, CRM data entry automation, and OCR pipelines.
The architecture · how it fits together
Everything you have in, one production system out.
These capabilities aren't sold separately. They compose into one system. Your raw data, documents, APIs and legacy systems flow through agents, pipelines and evaluation layers we architect together, and come out the other side as a system that runs reliably in production.
Running in production for
How we engage
From audit to live operation.
1-2 weeks
Audit & Readiness
Map workflows, data, and compliance requirements. Deliverable: AI Readiness Report & Roadmap.
4-12 weeks
Design & Implementation
Build custom AI stack, integrate with your existing infrastructure, and train your team.
Monthly retainer
AI Operations
Ongoing refinement, compliance monitoring, new capability deployment, and performance optimisation.
Know what you want to build?
Book a strategy callClient Work
Proof Points
Won and delivered engagements only.
AI Systems FAQ
Frequently asked questions
What AI and machine learning services does Bayseian offer?
Bayseian specializes in LLM deployment (GPT, Claude, Llama), agentic workflows, RAG architectures, model fine-tuning, AI guardrails, and production-ready AI systems for enterprises.
How long does it take to deploy an enterprise AI solution?
Typical enterprise AI projects range from 8-16 weeks depending on complexity, data availability, and integration requirements. We follow an agile approach with production-ready increments every 2-3 weeks.
Which AI platforms does Bayseian work with?
We work with all major AI platforms including OpenAI (GPT), Anthropic (Claude), AWS Bedrock, Azure OpenAI, Google Vertex AI, and open-source models like Llama, Mistral, and Falcon.
How do you ensure AI safety and compliance?
We implement comprehensive AI guardrails including content filtering, bias detection, output validation, audit logging, and compliance frameworks aligned with industry standards (GDPR, SOC 2, ISO 27001).
Which cloud platforms does Bayseian support?
We provide expert consulting for AWS, Google Cloud Platform (GCP), and Microsoft Azure, including multi-cloud and hybrid cloud architectures.
What is Infrastructure as Code (IaC) and why is it important?
IaC uses code (Terraform, CloudFormation, Pulumi) to manage infrastructure, enabling version control, automated deployments, consistency across environments, and rapid disaster recovery.
How can Bayseian help reduce cloud costs?
We optimize cloud costs through rightsizing instances, implementing auto-scaling, using spot/reserved instances, architecting serverless solutions, and establishing FinOps practices with continuous monitoring.
Do you provide Kubernetes consulting?
Yes, we specialize in Kubernetes architecture, EKS/GKE/AKS setup, cluster optimization, service mesh implementation (Istio), and migrating workloads to containerized environments.
What data engineering services does Bayseian provide?
We build ETL/ELT pipelines, data warehouses, real-time streaming analytics, data lakes, and modern data stacks using technologies like Apache Spark, Snowflake, Databricks, and dbt.
How do you handle real-time data processing?
We implement streaming architectures using Apache Kafka, AWS Kinesis, Apache Flink, and Spark Streaming to process millions of events per second with sub-second latency.
Which data warehousing platforms do you recommend?
We design solutions on Snowflake, BigQuery, Redshift, Databricks, and Synapse Analytics, selecting the optimal platform based on your data volume, query patterns, and budget.
How do you ensure data quality and governance?
We implement data quality frameworks with automated testing (Great Expectations, dbt tests), metadata management, data lineage tracking, access controls, and compliance monitoring.
Let's build your AI systems
Bring a workflow that matters. We'll come back with a point of view on what to build, what it's worth, and how we'd run it in production.