FOUNDRY

Production AI & Data Platforms

— streaming, lakehouse, and LLM systems built for real operations

We build Data & AI Foundries that deliver continuously: ingest → transform →govern → serve → observe → improve. We combine modern LLM/RAG + agentpatterns, strong software architecture, and automation (n8n + CI/CD + IaC) toproduce measurable business outcomes under real SLAs and securityconstraints.

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AI Value Factory

RAG/agents, eval harnesses, guardrails, feedback loops, cost controls

Streaming + Lakehouse Core

Kafka/CDC, Iceberg/Delta lakehouse, real-time analytics & serving

Reliability & Ops

SLOs, OpenTelemetry observability, incident playbooks, data quality gates

Delivery partner with operational ownership — not a "body shop"

We take responsibility for architecture, delivery, and ongoing operations. Our engineers ship production systems that handle throughput, privacy, reliability, and change management without turning your platform into fragile glue.

Architecture rigor: reference architectures, ADRs, clear boundaries, contracts, change-safe design

Data trust program: freshness SLAs, DQ monitors, lineage, ownership, incident workflow

LLM in production: evaluation, guardrails, privacy controls, audit logs, rollout safety

Automation-first execution: n8n workflows, CI/CD, IaC, golden paths, repeatable runbooks

Operational maturity: SLOs, alerting, on-call readiness, postmortems, continuous improvement

Why work with us

Six pillars that define how we deliver production systems

AI Foundry (not "one-off AI features")

We build repeatable pipelines that continuously deliver AI value with controlled risk.

Prompt libraries + versioning + approvals
Evaluation harness (offline + online), qualitymetrics, regression gates
Feedback loops: user signals → retraining/re-ranking/prompt iteration

LLM/RAG that is measurable and governed

Production LLM systems require retrieval quality, traceability, and monitoring.

Retrieval pipelines, chunking strategy, embeddings hygiene, access-aware indexing
Grounded answers, citations, trace IDs, and audit logs
Guardrails: policy enforcement, PII handling, jailbreak resistance, tool permissioning

Streaming that behaves like a product

Streaming is software engineering: contracts, backpressure, replay, idempotency, reliability.

Schema evolution strategy and compatibility rules
Quarantine/DLQ and deterministic reprocessing
Capacity planning, latency budgets, load testing and chaos drills

Lakehouse/warehouse with governance and cost discipline

Fast analytics requires contracts, ownership, and predictable spend.

Iceberg/Delta table design, partitioning, compaction, file sizing strategy
dbt tests + semantic layer patterns + metric definitions
Cost-per-query governance, performance tuning cycles, workload isolation

Automation as leverage (n8n + platform hooks)

We automate repetitive work and reduce MTTR with auditable workflows.

n8n workflows for data ops, QA gates, reporting, and alert actions
Slack/Jira/HubSpot/ServiceNow automations with approvals and logs
Self-serve "platform actions" (replay/backfill/rollout) with guardrails

Reliability, security, and cost are first-class constraints

Systems that "work" but fail in ops are not acceptable.

OpenTelemetry, SLOs, alert routing, runbooks, on-call readiness
IAM least privilege, secrets management, audit trails, environment separation
Cost/perf reviews, storage/query optimization, efficiency dashboards

Our delivery process

A proven approach to building production systems

Step 1 — Discovery & Risk Map

Business goals, productionconstraints, SLA/SLO targets, riskregister, current pain points

Step 2 — Architecture & Delivery Plan

target blueprint, ADRs, phased roadmap, validation strategy, ownership model

Step 3 — Build & Integrate

pipelines/services, IaC, CI/CD, tests, dashboards, security controls, automation workflows (n8n)

Step 4 — Run & Improve

incident readiness, reliability cycles, cost tuning, documentation + transfer

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Engineers who ship, operate, and continuously improve production systems

Senior Data Engineer (Lakehouse + Trust Layer)

Builds ingestion, governed lakehouse tables, transformation/testing layers, and reliability programs.

Core skills

Iceberg/Delta, Spark/Flink, dbt, Trino/Presto, orchestration, Terraform

Production focus

backfills, schema evolution, DQ gates, cost/perf tuning

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ML/LLM Engineer (RAG + Evals + Guardrails)

Ships LLM capabilities with retrieval pipelines, evaluation harnesses, and monitoring.

Core skills

RAG, embeddings, eval frameworks, safety/guardrails, prompt/config versioning, observability

Production focus

quality metrics, rollout safety, privacy controls, cost controls

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Engagement models

Flexible approaches to match your needs

Delivery Squad (Outcome Ownership)

Best for

platforms, migrations, continuous improvements

Includes:

tech lead + engineers + weekly governance, demos, metrics, operational readiness

Stability & Cost Sprint

Best for

incidents, runaway spend, low trust in data, unstable pipelines

Includes:

findings + prioritized roadmap + implemented quick wins + runbooks

AI Foundry Enablement

Best for

continuous AI feature delivery with eval gates and automation

Includes:

eval harness, guardrails, retrieval pipeline, n8n automation, monitoring, operating model

Use cases we solve

Real-world applications we've delivered

AI assistant for internal knowledge with measurable answer quality and policy controls

Real-time customer signals and ML feature pipelines with latency budgets

CDC from OLTP → lakehouse with replay, auditability, and governance

Data trust program reducing incidents and improving freshness SLAs

Warehouse/lakehouse and streaming compute cost optimization with predictable scaling

n8n-driven ops automations: incident workflows, reporting, approvals, governance actions

Frequently asked questions