SERVICES / AI & AUTOMATION

Put AI in production, not just in pilot

Governance, automated evaluation rigs, RAG architecture and guardrails built by senior engineers who ship workloads to production. Secure, compliant, and operational within one quarter.

DISCUSS AN AI PROJECT → EXPLORE CAPABILITIES

11 wks

Average time from concept to production-grade deployment

99.4%

Retrieval precision on enterprise RAG benchmarks

100%

HIPAA & SOC 2 compliant architecture adherence

Throughput speedup with optimized tokenization & caching

CORE CAPABILITIES

Engineered for reliability, evaluated on real data

Enterprise RAG & Semantic Retrieval

High-precision hybrid retrieval systems combining vector embeddings with BM25 keyword search, reciprocal rank fusion, and rerankers. Built for strict data privacy and zero hallucination tolerances.

Vector DBs Hybrid Search Rerankers Chunking Strategies

Autonomous Agentic Workflows

Multi-agent orchestration frameworks where specialized agents collaborate on complex business logic, perform structured tool calling, and execute multi-step database and API transactions safely.

LangGraph CrewAI Tool Calling Human-in-the-Loop

Guardrails, Safety & Governance

Production-grade guardrails for input/output sanitization, PII redaction, prompt injection defense, policy enforcement, and full audit trails meeting enterprise compliance requirements.

PII Redaction Injection Defense Audit Trails Policy Enforcers

Automated Evaluation Rigs

Automated test suites (Ragas, DeepEval) that evaluate retrieval precision, context recall, faithfulness, and answer relevance on every commit before model updates reach live production.

Ragas & DeepEval CI/CD Regression Ground Truth Sets Latency Benchmarks

Fine-Tuning & Model Optimization

Domain-specific parameter-efficient fine-tuning (LoRA, QLoRA) on open-weights foundation models, paired with vLLM and TensorRT-LLM for massive throughput boosts and reduced inference costs.

QLoRA / PEFT vLLM & TensorRT Quantization Custom Tokenizers

Observability & Continuous Monitoring

End-to-end tracing, token usage attribution, cost tracking, drift detection, and automated feedback loops integrating Langfuse, Phoenix, and OpenTelemetry directly into your dashboards.

Langfuse / Arize Cost Attribution Drift Alerts Telemetry Tracing

WHY PILOTS FAIL

Moving from demo prompts to deterministic systems

Most AI demos look impressive until they hit edge-case schemas, PII compliance boundaries, or unpredictable latency spikes under real user concurrency.

Bridgewave embeds automated evaluation rigs into continuous integration — treating prompt regressions and hallucination drift with the same rigor as breaking unit tests.

Automated regression test runs on every prompt and model change

Granular token usage tracking and unit-cost economics per query

Zero lock-in: fully owned infrastructure within your VPC tenant

OUR DELIVERY LIFECYCLE

From raw data to resilient production

Discovery & Feasibility Audit

We evaluate your data sources, security boundaries, compute budget, and define unambiguous pass/fail evaluation metrics for production deployment.

Architecture & Guardrail Design

Our engineers architect the retrieval pipeline, tool schemas, token limits, and compliance guardrails tailored to your infrastructure requirements.

Squad Deployment & Rig Testing

A dedicated Bridgewave AI pod builds the pipelines, integrates APIs, runs automated evaluation benchmarks, and validates edge cases.

Production Rollout & Handover

Zero-downtime deployment with live tracing, latency alerting, and comprehensive documentation so your internal team takes full ownership.

TOOLING & ECOSYSTEM

Production-tested AI technology stack

MODELS & LLMs

OpenAI GPT-4o, Anthropic Claude 3.5, Meta Llama 3, Mistral, AWS Bedrock

ORCHESTRATION

LangChain, LangGraph, LlamaIndex, DSPy, Semantic Kernel

VECTOR DATABASES

Pinecone, Qdrant, Weaviate, pgvector, Milvus, Chroma

EVAL & OBSERVABILITY

Langfuse, Ragas, Arize Phoenix, DeepEval, OpenTelemetry, MLflow

INFRASTRUCTURE

Docker, Kubernetes, Ray Serve, vLLM, Triton, AWS, Azure, GCP

FAQ

Frequently asked questions on AI adoption

Have questions specific to your dataset or regulatory posture? Talk to our practice leads.

We prioritize private cloud deployments (VPC endpoints on AWS Bedrock, Azure OpenAI, or self-hosted open models on private Kubernetes clusters). All data is encrypted in transit and at rest, and zero client data is ever used for model training.

We build automated evaluation rigs that benchmark retrieval precision, factual consistency, latency percentiles (p95/p99), and safety guardrails against a curated ground-truth dataset before any release is promoted to production.

Yes. Our squads work inside your GitHub/GitLab repositories, your cloud tenants, and your CI/CD pipelines. Every contract ends with clean code, architectural diagrams, and a full team knowledge transfer.