AI features stuck in experiments
Promising prototypes haven't become useful product capabilities.
Forward-Deployed AI EngineeringEmbedded expertise. Lasting capability.
Move AI From
Idea to
Production.
Embed experienced AI engineers directly with your team to build, deploy, and transfer production AI systems—without spending months building every capability internally.
Book an AI Production AssessmentIdentify the highest-value opportunity, what is blocking production, and the fastest practical path forward.
One shared delivery teamInside your business
Your product &
engineering team
engineering team
Our embedded
AI engineers
AI engineers
Production AI.
Owned by your team.
Expertise stays with you.Ownership, by design.
For teams investing in AI
Software companiesEngineering organizationsIT consultanciesBusiness teams
01 / The production gap
AI isn't short on ideas.
It's short on production.
The challenge is turning experimentation into something that actually works inside your business. Sound familiar?
Coding tools without a system
Developers use Claude Code, Codex, Cursor, or Copilot individually.
Agents that aren't production-ready
Automations need integration, evaluation, and a reliable operating model.
AI demand beyond delivery capacity
Your customers want AI solutions your team isn't ready to deliver.
Expensive model workloads
General-purpose models are driving unnecessary cost and latency.
Specialized talent still missing
Hiring every AI capability internally takes time you may not have.
Move forward with experience across AI, software engineering, product, architecture, and implementation—working directly with the team responsible for the outcome.
See how we work02 / The engagement
One engagement.
From opportunity to ownership.
We embed directly with your team and work inside the problem. Hands-on implementation, from the first decision to the handover.
Discover
Understand your product, users, workflows, data, systems, constraints, and business objective.
Build
Design and implement the AI system, agent, workflow, model, or engineering harness required.
Deploy
Integrate with your real product, infrastructure, development process, or customer environment.
Train
Enable your engineers, product teams, and users to operate and improve what we've built.
Transfer
Document the system and hand over the architecture, knowledge, and ownership to your team.
Embed. Build. Deploy. Train. Transfer.Book an AI Production Assessment
03 / Where we help
The outcome stays the same.
The bottleneck changes.
Forward-Deployed AI Engineering adapts to the production problem in front of you. Find the one that matches yours.
Your product needs to become AI-native.
Make AI part of the core experience of your existing SaaS or software product.
We identify the right workflows, design the AI experience, architect and build the system, integrate it, evaluate it, and get it production-ready.
The outcomeUseful AI capabilities customers can actually use.
Your team uses AI. Now give it a system.
Turn individual productivity hacks into repeatable engineering infrastructure.
We build context systems, skills, agents, automated testing, code review, MCP integrations, development harnesses, CI/CD workflows, and engineering standards around your team.
The outcomeSystematically ship more with your existing team.
Your customers want AI. Be ready to deliver.
For IT consultancies and software-services companies, we act as the AI capability behind your existing customer relationship.
Your team owns the customer and commercial relationship. We contribute discovery, presales, architecture, PoCs, implementation, and embedded AI engineers when needed.
The outcomeMore confidence winning and delivering serious AI engagements.
Your AI works. But it costs too much.
Large frontier models aren't always the best fit for every specialized workload.
Where the workload supports it, we benchmark alternatives, prepare datasets, fine-tune smaller models, optimize inference, evaluate task performance, and deploy private, task-specific systems.
The outcomeMore efficient production AI with task performance evaluated.
You need the capability to stay in-house.
Build the foundations for your organization to own future AI initiatives internally.
We help establish architecture standards, development processes, hiring requirements, technical evaluations, onboarding, training, and team structures.
The outcomeInternal AI capability that continues after the engagement ends.
Start with the production problem.
Then choose the technology.
We choose the architecture, workflows, and models around the improvement your business needs.
What should improve?
RevenueProduct valueEngineering throughputDelivery speedAI costCustomer experienceOperational efficiency
04 / Previous technical work
Built for real production work.
Selected outcomes from previous work across AI systems and engineering workflows.
~25 sec to~500 ms
Inference latency on a production AI workload.
5–6 days to12–16 hrs
Development cycles on suitable engineering workflows.
Training costs4–6×
Reduction in model-training costs.
Built & evaluated15+
LLM, SLM, and VLM systems.
Production AI requires
more than an AI model.
Enough depth across disciplines to solve your production problem end to end.
AI
LLMs · SLMs · agents · RAG · fine-tuning · evals · guardrails · distillation · quantization · inference optimization
Software Engineering
Python · FastAPI · APIs · Docker · Kubernetes · AWS · CI/CD · vector databases · backend systems · production architecture
Agentic Engineering
Claude Code · Codex · MCP · skills · commands · custom agents · context engineering · AI testing · code review · development harnesses
Product
Workflow analysis · AI product strategy · AI-native UX · feature prioritization · architecture · modernization
06 / Start with one opportunity
Start small.
Prove the direction.
Expand from there.
Move the right AI opportunity into production and leave your team stronger than when we started.
Book an AI Production AssessmentThe starting point
One high-value opportunity.
Clear acceptance criteria.
Identify the product, workflow, engineering bottleneck, delivery challenge, or AI workload worth solving first.
The next step
Expand only when the problem justifies it. That could mean an embedded engineer, an AI pod, architecture work, engineering transformation, model optimization, or team enablement.
A good fit if…
- You have a product, engineering organization, customer workflow, or meaningful AI workload.
- AI is an active business priority with a real problem worth solving.
- You want implementation and have an internal stakeholder to own the initiative.
- You want your team to understand and eventually own what gets built.
07 / Your questions
Before we
work together.
How embedded delivery fits into your team, your tools, and your long-term ownership.
We already have engineers. Why would we need this?
That's usually the ideal situation. Forward-Deployed AI Engineers work alongside your existing team, adding specialized AI, architecture, product, and production experience where needed. We ship the initial systems together and transfer knowledge internally.
We want AI capability in-house.
So do we. The model is designed around Build, Deploy, Train, and Transfer. Documentation, architecture handover, training, and internal ownership are part of the engagement.
Our developers already use Claude Code or Codex.
Individual tool adoption and an organization-wide agentic engineering system are different things. We standardize context, skills, agents, testing, reviews, integrations, and development workflows so improvements aren't dependent on a few individual developers.
Why not just hire an AI engineer?
You can. Hiring takes time, and one individual may not bring AI architecture, production engineering, product thinking, specialized model knowledge, and implementation experience together. A Forward-Deployed engagement can deliver the initial capability while you determine what should live permanently in-house.
We don't want consultants.
The work is implementation-led. Forward-Deployed Engineers work directly inside your delivery process and ship systems alongside your team. The engagement carries through to deployment and handover.
We don't want another vendor dependency.
The goal is to make your organization more capable. Documentation, knowledge transfer, training, architecture handover, and internal ownership ensure your team can operate and improve what gets built.
What kind of AI project can we bring you?
Bring the production problem: an AI product feature, agentic workflow, engineering productivity challenge, customer AI project, expensive LLM workload, architecture problem, or internal capability gap. We'll determine whether Forward-Deployed AI Engineering is the right fit.
Your next production opportunity
Stop letting good AI ideas die between prototype and production.
Identify the right opportunity.
Get it shipped.
Keep the capability.