Core Delivery

Build, stabilize, and move with engineering clarity.

Software Engineering

Platforms and products that must stay fast, safe, and worth running.

We design, audit, and rebuild systems that have to hold in production.

Architecture that scales

Reliability, performance, and security together

Senior engineers.

Security Audit

We audit desktop software, mobile apps, backend services, AI features, APIs, embedded surfaces, and the trust boundaries between them as one real system.

We review desktop clients, mobile apps, services, binaries, APIs, AI workflows, and operational assumptions together instead of pretending each layer can be secured in isolation.

Desktop, mobile, backend, and AI reviewed together

Findings tied to exploit paths and business impact

Practical fixes, retesting, and buyer-ready evidence

Ransomware Recovery

We help teams recover after ransomware (crypto lockers / encryptors) with a calm, evidence-led track: stop the spread, validate what is encrypted, attempt safe decryption when…

Ransomware recovery is not just decrypting files.

Clear recovery plan in the first working day

Safe ransomware decryption feasibility (when decryptors exist, we verify and apply them carefully)

Restore and rebuild across endpoints, servers, NAS/storage, and virtualization

Consulting

Architecture, modernization, research, security, AI, migration, and performance decisions for teams that cannot afford the wrong bet.

We align architecture, modernization, performance, security, delivery reality, and domain constraints with business goals.

Clear tradeoffs

Decision-ready next steps

Useful before costly commitments

PoC Engineering

We build proof-of-concept systems across AI, software engineering, reverse engineering, embedded work, security research, and difficult integrations when the team needs evidence before committing to a…

A good PoC should show whether the concept works, where it breaks, what it depends on, and whether the path deserves a larger build.

Built to answer decision-critical questions in any engineering domain

Good fit for research tracks, product bets, integrations, and system modernization

Feasibility, performance, security, and operability tested early

AI Systems

AI systems with control, economics, and production discipline.

Agentic AI Engineering

We design and harden agent workflows that call tools, make bounded decisions, and stay usable in production.

We turn agent ideas into systems that stay useful, bounded, observable, and economically sane.

Multi-step orchestration that survives real boundaries

Evaluations before rollout

Guardrails for high-risk actions

AI Security & Governance

We secure LLM features and agent workflows with threat models, authorization, data boundaries, and auditability.

AI governance is about enforceable boundaries: who can authorize it, what it can access, and what it can execute.

Prompt injection and tool abuse treated as system risks

Policy design for permissions and escalation paths

Evidence leadership and engineering can both use

AI Data Leakage Prevention

We design and audit the data boundaries around AI systems so sensitive information stays out of prompts, retrieval, memory, logs, and model outputs.

We look at how sensitive data enters the system, what can retrieve it, how long it lives, what agents can do with it, and how it…

Focused on practical leakage paths, not abstract policy alone

Useful for both customer-facing AI and internal automation

Covers retrieval, prompts, agent actions, and output handling together

Inference Optimization

We optimize serving stacks for AI products where response time and GPU spend are already business problems.

Response time, serving efficiency, and infrastructure discipline decide whether the feature survives scale.

Latency and cost treated as one system

Routing, caching, batching, and serving strategy together

Observability that shows where margin leaks

Autonomous AI Systems Deployment

We take multi-step AI systems from promising prototypes to controlled production workflows with integrations, approvals, observability, rollback, and cost discipline.

We focus on the hard parts of deployment: how tools are called, how state is handled, how retries behave, where approvals are inserted, how failures are…

Built for real production rollout, not demo-stage orchestration

Focus on reliability, traceability, and operational control

Useful for internal automation and customer-facing AI products

Deep Engineering

Deep systems work when performance, trust, or visibility breaks higher up.

Low-Level Engineering

Native engineering for runtimes, SDKs, endpoint components, device software, and systems that need real control.

We use native APIs and platform internals when the usual stack is too slow, too opaque, or too limited.

Measurement before guesswork

Platform-specific expertise

Clear tradeoffs before invasive work

HFT Engineering

Trading infrastructure for teams that care about p99.9, replay, recovery, and real market conditions.

We design, audit, and rebuild trading infrastructure for teams competing at microsecond precision.

p99 and p99.9 over vanity averages

Protocol, NIC, timing, and software-path thinking

Performance that survives production reality

Kernel Engineering

We build kernel-mode components for endpoint security, device software, observability, and performance-critical paths.

We design kernel components for products that need native performance, stronger local control, and tighter observability.

Design, rollout, debugging, and rollback discipline

Compatibility and update safety first

Performance measured against stability

Reverse Engineering

We reverse engineer firmware, desktop software, embedded components, update packages, and opaque binaries when documentation is missing, trust is uncertain, or behavior has to be proven…

We examine firmware images, binaries, installers, drivers, and software packages to reconstruct what the system does, how it communicates, what it stores, and where the risk…

Strong fit for embedded products, vendors, and inherited systems

Useful in diligence, incident response, interoperability, and security research

Firmware and software analyzed as connected operational systems

Clients Across Key Engineering Markets

Spain, Germany, the Netherlands, Italy, Poland, Ukraine, the United States, Singapore, and Japan.

World map highlighting SToFU client presence across Europe, Ukraine, the United States, Singapore, and Japan.

Engineering Breadth

One team for software, AI, and systems that move work forward.

We work across product engineering, neural systems, low-level software, frontier prototypes, and the security and privacy controls serious buyers now expect around AI and critical software.

Software & Platform Engineering

Application and systems development that ships under pressure

This is the delivery core: software engineering, platform work, APIs, distributed systems, performance tuning, and the sort of native depth needed when reliability and speed are part of the product.

Domain Software Delivery product engineering
Domain Distributed Systems platform scale
Domain API & Backend service architecture
Practice Performance Engineering latency and throughput
Stack C++ / Rust native systems
Practice Platform Modernization rewrite or recovery

AI, Neural & Agent Systems

Neural-network and AI engineering beyond demos and wrappers

We build applied AI systems where models, prompts, retrieval, orchestration, inference economics, and runtime control have to work together as one production system.

Domain Neural Inference model execution
Domain RAG Systems retrieval workflows
Domain Agentic Workflows tool orchestration
Practice Prompt & Tool Control runtime discipline
Practice Inference Optimization cost and latency
Practice AI Evaluation quality and drift

Prototypes, Research & Quantum

PoCs for serious product bets, research tracks, and frontier computing

Some work begins before the roadmap is clear. We build technical prototypes, research implementations, and exploratory systems when clients need proof, feasibility, or a sharp read on a hard direction.

Format Technical PoCs fast validation
Format Research Builds applied exploration
Format Prototype Systems product direction
Frontier Quantum Computing algorithmic exploration
Practice Feasibility Studies go / no-go clarity
Practice Experimental Tooling proof before scale

Security, Privacy & AI Trust

Cybersecurity for AI, software, data, and critical systems

Security is still part of the stack: software audits, AI-specific abuse paths, reverse engineering, data-leak prevention, and the trust controls serious buyers expect around modern systems.

Domain Security Audits desktop, mobile, backend
Domain AI Security models and agents
Domain Data Leakage Prevention sensitive boundaries
Practice Reverse Engineering binary and firmware
Standard Privacy & GDPR data discipline
Practice Threat Modeling design-level risk

How Engagement Starts

Most serious buyers do not start with a full scope.

They start with one system under pressure and need a technical read they can trust.

Latency or cost pressure Security or delivery risk AI rollout under scrutiny
01

Bring the bottleneck

Bring the system that has started hurting delivery, trust, margin, or uptime.

03

Move with a credible next step

Leave with clearer scope, sharper priorities, and a next move the business can actually act on.

Technical Blog

Swipe to explore more articles

Vercel April 2026 Security Incident: Context.ai OAuth Compromise, Exposed Environment Variables, and What Teams Should Do Next

Vercel April 2026 Security Incident: Context.ai OAuth Compromise, Exposed Environment Variables, and What Teams Should Do Next

A clear incident brief and response checklist for teams shipping on Vercel. What is confirmed, what is unknown, what to rotate, and how to reduce OAuth blast radius.

Reverse Engineering in the AI Era: Why the Work Matters More, and How AI Changes the Workflow

Reverse Engineering in the AI Era: Why the Work Matters More, and How AI Changes the Workflow

A practical article on why reverse engineering became more valuable in the AI era, where AI accelerates the work, and where human validation still decides the answer.

C++, Rust, and the Windows Kernel: Where Safety Helps and Boundaries Still Bite

C++, Rust, and the Windows Kernel: Where Safety Helps and Boundaries Still Bite

A practical read on where Rust helps in Windows low-level work, where C++ still remains the default, and why the real design problem is the boundary.

C++, Rust, and High-Frequency Trading: Where Deterministic Latency Decides the Argument

C++, Rust, and High-Frequency Trading: Where Deterministic Latency Decides the Argument

A practical article on where C++ still owns the hot HFT path, where Rust genuinely helps, and how disciplined teams draw the boundary between them.

Killing 360 Reviews: How We Stopped Rating People and Started Managing Work

Killing 360 Reviews: How We Stopped Rating People and Started Managing Work

A field note on why 360 reviews damaged trust in small teams, what they hid from managers, and what replaced them instead: delivery metrics, transparent status, and work-based management.

Technical PoC Engineering for Frontier Systems: When a Prototype Should Earn the Next Step

Technical PoC Engineering for Frontier Systems: When a Prototype Should Earn the Next Step

A practical guide to technical PoC engineering for frontier systems, showing how prototypes earn confidence, expose risk, and justify the next move.

Secure OTA for Embedded and AI Devices: Updating Without Breaking Trust

Secure OTA for Embedded and AI Devices: Updating Without Breaking Trust

A guide to secure OTA update design for embedded and AI devices, including signatures, staged rollout, rollback rules, and field-safe delivery.

Safe C++ Rust Interop: FFI Boundaries That Do Not Rot Under Delivery Pressure

Safe C++ Rust Interop: FFI Boundaries That Do Not Rot Under Delivery Pressure

A deep dive into safe C++ and Rust interop, ABI boundaries, ownership rules, diagnostics, and integration patterns that survive long-running delivery.

Explore the Full Technical Blog

Explore the Full Technical Blog

Open the technical blog for the full archive of engineering notes on AI systems, low-level software, security, testing, and production architecture. More guides, more categories, and every article live there.

Privacy-disciplined delivery

Built to move serious systems forward with privacy held close to the work.

When delivery touches customer data, employee data, regulated workflows, or cross-border operations, privacy stays aligned with the engineering path from the start.

Delivery Privacy-disciplined delivery Security and privacy stay in the same lane across the build path, review path, and data path
Frameworks GDPR, UK GDPR, CCPA/CPRA, PIPEDA Handled as real buyer and legal requirements, not afterthoughts
Contracts DPA / SCC-ready Structured for cross-border safeguards when the engagement needs them

Contact

Start the Conversation

A few clear lines are enough. Describe the system, the pressure, the decision that is blocked. Or write directly to midgard@stofu.io.

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