Production-Grade AI Engineering

Engineering
Intelligence.

AI systems that hold under production pressure.

We build agents and the data infrastructure that feeds them — with governance from day one.

[ SUBMIT SPECS ] [ SEE OUR WORK ]

No SDRs. A Principal Engineer reviews every submission.

// Deploying unified stack: agents + data + governance

$ langgraph deploy --checkpoint redis://prod:6379

$ kafka-connect start --config cdc-pipeline.json

[00:00:01] CDC stream: 14 sources connected

[00:00:03] Agent pipeline: 12 agents orchestrated

[00:00:04] Governance: HITL gates enabled

>> SUCCESS: Unified stack deployed

>> p99 latency: 38ms | 800 concurrent sessions

Trusted Architecture By

Authority Proof

Frontier R&D Lab

A private applied AI lab where AW tests multi-agent systems against real operating constraints: routing, memory, governance, review, trust, security, feedback, and knowing when not to automate.

Explore the lab →

Fit

Who We Work With

Technical leaders — from funded founders to enterprise platform teams — where architecture mistakes are expensive.

✓Technical founder, CTO, or VP Engineering with a system already underway and real delivery pressure
✓Team that needs principal-level architecture judgment, not ticket execution or project management
✓AI-core product where agent reliability, RAG quality, data infrastructure, or observability are real constraints
✓Buyer willing to hear "do not build it that way" and act on the recommendation

Engagement Tracks

How Teams Engage

Choose the entry point that fits.

Validate

Production Audit / Architecture Review

Fixed-fee architecture review. Know exactly where the system stands and what needs to change before build compounds.

See engagement → Build

Scoped Build Sprint

One defined workstream, architecture through deployment. Typical sprint: 4-12 weeks from kick-off to production handoff.

See engagement → Stabilize

Stabilization Sprint

Bounded rescue for systems already straining. We scope in days, not weeks. Corrective engineering starts immediately.

See engagement → Embedded Advisory

Embedded AI Advisory

Ongoing architecture counterpart for teams making active design decisions. Monthly cadence, async review between sessions.

See engagement →

Technical Depth

Where we go deep

The technical systems behind the work.

Primary

AI Agents & Autonomous Systems

Agent systems with checkpoints, approvals, and production-grade observability.

$ langgraph deploy --agents 12 --checkpoint redis
✓ Pipeline active · p99: 38ms · 800 concurrent
✓ HITL approval gate enabled

LangGraph CrewAI AutoGen LangSmith

Data Engineering

Real-time pipelines, CDC ingestion, and event systems that hold up under load.

events_per_day: 2.4M

Kafka Flink Spark dbt

Advanced RAG Systems

Retrieval systems, evaluation loops, and production-grade grounding.

LlamaIndex Pinecone

ML & Data Science

Models turned into monitored production systems.

PyTorch MLflow scikit-learn

Vector & Graph Databases

Retrieval and knowledge infrastructure for AI-native applications.

Pinecone Weaviate Neo4j

Full-Stack AI Applications

From inference endpoint to interface, with deployment and operational guardrails in place.

FastAPI K8s Terraform Docker React

Strategic AI Architecture

Architecture review, suitability decisions, and governance framing before build compounds around the wrong pattern.

Suitability Governance

// Production benchmarks from live deployments

p99_checkpoint_latency: 38ms

Checkpointing stays fast enough for retries and approvals under load.

time_reduction_vs_manual: >95%

Replaced manual operating work, not just presentation effort.

events_processed_daily: 2.4M

Production scale with real downstream dependencies.

alert_accuracy_improvement: 73%

Changed an operational decision path a team actually relies on.

Field Notes

What We Keep Seeing Go Wrong

The failure mode is usually not model quality. It is architecture drift: too much novelty, not enough operational discipline.

Agents

Teams reach for multi-agent orchestration when a deterministic workflow or a single structured-output step would be cheaper, safer, and easier to operate.

Data

Streaming systems are introduced late, after product logic has already been built on brittle APIs, manual exports, and undocumented schemas.

Observability

Teams monitor infrastructure but not decision quality, checkpoint failures, retry behavior, or tool-call boundaries, so failures look random when they are not.

Delivery

Architecture review is skipped to move faster, then the team spends the next two months undoing stack choices that were wrong in week one.

Evidence

Proof, Not Promises

Start with the problem closest to yours.

Stuck Post-POC

Your agent demo works. The production path does not. Checkpoints, approvals, failure handling, and observability designed into the system before launch pressure compounds.

Read the proof → RAG Quality Collapse

Retrieval answers confidently but incorrectly. Chunking, ranking, grounding, and evaluation need architectural attention — not more prompt tuning.

Read the proof → Data Under Load

Event volume, latency, and downstream dependencies outgrew the batch pipeline. Streaming infrastructure needs production-grade architecture.

Read the proof → Enterprise AI Portfolio

Multiple AI initiatives competing for budget. Architecture triage, governance framing, and vendor evaluation before the roadmap hardens.

Read the proof →

Process

How We Work

Every engagement leaves behind explicit decisions, review criteria, and rollout checkpoints. Our methodology codifies what we learned shipping 12 production systems.

Technical Discovery

We audit your current stack, identify constraints, and map the shortest path from prototype to production.

1-2 days

Audit & Recommendation

Architecture findings and a direct recommendation: production audit, embedded advisory, scoped build sprint, or a clear signal to pause.

3-5 days

Build & Ship

Weekly deployments to staging. Code reviews, CI/CD, observability baked in from day one.

2-12 weeks

Handoff & Review

Handoff with runbooks, dashboards, and explicit ownership. Your team owns the system fully, with ongoing review support only where it improves decision quality.

Ongoing

Production-Ready
AI Agents

A Developer's Guide to Building Scalable, Reliable & Observable AI Agents

Igor Bobriakov

Authority

Our operating model is published.

Igor Bobriakov authored Production-Ready AI Agents. The same operating discipline — checkpoints, observability, failure handling — shows up in every system we ship.

View on Amazon ↗

Client Feedback

What Our Clients Say

"ActiveWizards has one of the best combinations of skills, professionalism, and value that I have seen."

Nitin Borwankar CEO, Numericc Applications

"ActiveWizards is an amazing team of data scientists. They helped us understand our project goals and crafted solution to help us achieve those goals. They are highly recommended."

Jamison Hill Owner, Collarbone

"It was a pleasure to work with ActiveWizards. With their expertise each of my projects has been personalized and the end product has always exceeded my expectations by a wide margin."

Lawrence Burns Professor & Vice Chair FSBC, GVSU

"I used the service of ActiveWizards for building an architecture visualization tool. Their team was always helpful and provided the best quality service. I had an exceptional experience and will hire them again."

Syed S Ahmad CEO, Capability Management

"I am really enjoying working with ActiveWizards. Their skill, commitment and technical abilities coupled with an excellent level of project management are making this an enjoyable project."

Garrett Reynolds Technical Project Manager, ArtOfUs

"We had great experience working with ActiveWizards. They approached our task with diligence and creativity providing us with an excellent solution, which is easy to use. Highly recommended."

Alina Aseeva Corporate Development Manager, 3 Plus TV Network AG

"Active Wizards took us through their process from start to finish and we got a better outcome than we had expected. I'll definitely use your services again and recommend you to my friends."

Jean Patrick Bisson Co-founder, Growth Label

"Very capable team who communicate well, and make every effort to get things right."

Jack Donaldson Founder, Peak Media & Consulting

"Excellent quality, blended with fast response. I highly recommend them."

Petru Faurescu Technical Project Manager, API Maintenance Systems

"The team is friendly and they know what they are talking about. Looking at their portfolio you can see that they are well versed in their field. It's great to work with ActiveWizards and just by talking to them you can see that they have the capability to execute your project."

Cem Yılmaz CEO, NakitCoins

"I look forward to work again in the future. It was a good experience!"

Carlos Pinho Owner, TekTuts

"Great team to work with. Understands and able to transform ideas into working products."

Abid Ali Managing Director, MarchUp Consulting

"ActiveWizards is a team of professionals who comprehend the scope and complexity of our project, delineate it into simplified deliverables and, finally, unify each task to produce an end product of the highest quality."

Kefim Nancoo Business Development Manager, Joova

"Outstanding!"

Christopher Muffat CEO, Dathena Science

"These guys are fast, efficient and fair. They have turbo charged our CRM and client communications. Great team."

Jason Dittmer General Manager, The Mountain Guides

Ready to ship?

Let's build your production AI system

Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.