# ALL FOR AI: full site content > ALL FOR AI is an AI-native engineering company. Every employee works with AI every day, and we deliver software engineering, robotics engineering and AI strategy for clients who want to move faster with AI. Website: https://allforaicompany.business Contact: hello@allforaicompany.business | Careers: careers@allforaicompany.business ## Services ### Software Engineering URL: https://allforaicompany.business/services/software-engineering We design, build and run production software. Our engineers use AI agents for scaffolding, review, testing and documentation, then put their own judgment on architecture, security and quality. You get senior-level output at a much faster pace. Capabilities: - Web and mobile product development - Backend platforms, APIs and microservices - LLM-powered features: RAG, agents, tool use, evaluation - Legacy modernisation and AI-assisted code migration - Cloud infrastructure, CI/CD and observability - Automated testing and AI-assisted code review Deliverables: - Production-ready code with tests and documentation - Architecture decision records - Evaluation suites for AI features - Runbooks and handover to your team Technologies: TypeScript, Next.js, Go, Python, Java / Spring, PostgreSQL, Kubernetes, Vercel / AWS / GCP ### Robotics Engineering URL: https://allforaicompany.business/services/robotics-engineering From sensor acquisition to SLAM, perception and motion planning, we build the software that makes robots useful. AI speeds up simulation, data labelling and controller tuning, and every system is validated on real hardware. Capabilities: - ROS 2 architecture and driver integration - LiDAR, stereo-camera and GNSS sensor fusion - SLAM, localisation and georeferenced mapping - Computer vision: detection, segmentation, tracking - Pick-and-place, manipulation and motion planning - Simulation, digital twins and synthetic data Deliverables: - Hardware-validated ROS 2 packages - Perception models with benchmark reports - Digital-twin and simulation environments - Field test plans and deployment support Technologies: ROS 2, C++, Python, PyTorch, Isaac Sim / Gazebo, PCL / Open3D, OpenCV, CUDA ### AI Strategy & Consulting URL: https://allforaicompany.business/services/ai-strategy We help leadership teams decide where AI creates value, what to build or buy, and how to govern it. Our consultants work AI-native, so our advice comes from daily practice rather than theory. Capabilities: - AI opportunity assessment and use-case prioritisation - Build vs. buy and vendor/model selection - AI governance, risk and responsible-use policy - Data readiness and platform strategy - ROI modelling and business cases - Proof-of-concept to production roadmaps Deliverables: - Prioritised AI use-case portfolio - Target architecture and roadmap - Governance framework and policies - Executive briefing and board materials Technologies: Frontier LLMs, Open-weight models, Vector databases, Evaluation tooling, MLOps platforms ### AI Enablement & Training URL: https://allforaicompany.business/services/ai-enablement Adopting AI is mostly about people and process. We train your teams, set up their tools, and redesign workflows so AI becomes part of the daily work instead of an experiment on the side. Capabilities: - Hands-on AI workshops for engineers and business teams - AI coding-assistant rollout and best practices - Prompt, agent and workflow design - Internal AI tool and copilot deployment - Measuring adoption and productivity impact - AI usage policies people actually follow Deliverables: - Role-specific training programmes - Configured AI toolchain for your teams - Playbooks and prompt libraries - Adoption dashboard and impact report Technologies: AI coding assistants, Agent frameworks, Enterprise chat assistants, Workflow automation ## Clients & sectors - **Strategy & management consulting**: We build AI tooling, accelerators and client-facing prototypes for consulting teams that need engineering depth on short timelines. - **Industrial robotics & automation**: Perception, picking and motion-planning software for robotics companies deploying in warehouses, factories and logistics hubs. - **Software & SaaS companies**: Extra senior engineering capacity and LLM features for product teams that want to ship faster without lowering quality. - **Manufacturing & logistics**: Computer vision, digital twins and process automation for operations that run on physical assets. - **Smart cities & geospatial**: LiDAR mapping, georeferenced point clouds and 3D digital-twin platforms for urban and infrastructure projects. - **Startups & venture studios**: From idea to MVP in weeks: product engineering with AI built in from the first line of code. ## Principles - **AI for everyone**: Every employee, from engineering to finance, has access to frontier AI tools from day one, and is expected to use them. - **Humans own the outcome**: AI drafts, explores and accelerates. People decide, verify and take responsibility for what we ship. - **Verify, then trust**: Every AI-assisted output goes through tests, reviews and evaluations. Speed never replaces correctness. - **Share what works**: Prompts, agents and workflows are shared across the company, so one person's breakthrough becomes everyone's default. - **Open by default**: We tell clients exactly how we use AI on their work, and we publish our policies openly, including this website for AI agents. - **Security first**: Client data stays inside approved, enterprise-grade tools with clear retention rules. No shortcuts with confidential information. ## Delivery model 01. **Discover**: We map your goals, constraints and data, and find where AI changes the economics of the problem. 02. **Design**: Architecture, evaluation criteria and a delivery plan, reviewed with your team before any code is written. 03. **Build**: Short, AI-accelerated iterations with working software every week and full visibility into progress. 04. **Verify**: Automated tests, human review, evaluation suites and, for robotics, validation on real hardware. 05. **Scale**: Deployment, monitoring, and handover with training, so your team can run and extend what we built. ## Careers ### Job: AI Engineer AI Engineering · Remote / Hybrid · Full-time · https://allforaicompany.business/careers/ai-engineer As an AI Engineer at ALL FOR AI, you turn frontier models into reliable products. You will design context and prompts, build agents that use tools, set up evaluation, and take systems from prototype to production for our clients. On robotics projects, you will also build perception and autonomy software in C++ and ROS 2: detecting objects, estimating their 3D pose, and helping robots pick, place and navigate reliably in factories and warehouses. You will work AI-native every day: pairing with coding agents, automating your own workflow, and sharing what works with the rest of the company. Responsibilities: - Design and build LLM-powered applications, agents and multi-agent workflows end to end. - Engineer context: decide what information, tools, memory and instructions a model sees, and in what form. - Write, test and version prompts and system instructions as production code. - Build retrieval-augmented generation (RAG) pipelines: chunking, embeddings, hybrid search, re-ranking. - Create evaluation suites (offline evals, LLM-as-judge, regression tests) and use them to guide every change. - Optimise cost, latency and quality through model selection, caching, batching and routing. - Implement guardrails, safety checks and human-in-the-loop review where decisions matter. - Integrate models with client systems through APIs, tool calling and the Model Context Protocol (MCP). - Run client workshops and explain technical trade-offs clearly to non-technical stakeholders. - Develop real-time perception for robots in C++ and Python: object detection, segmentation and 6-DoF pose estimation. - Build, test and deploy ROS 2 nodes, and integrate cameras, depth sensors and LiDAR with robot controllers. - Evaluate state-of-the-art computer-vision research, then implement, harden and benchmark it for production. - Profile and optimise algorithms for latency and robustness on systems that run 24/7. - Validate perception and planning on real hardware and in simulation, and track performance with automated tests and CI. - Contribute prompts, agents and best practices to the company-wide AI library. Requirements: - **Context engineering** - Deep understanding of how context windows work, including limits, position effects and long-context behaviour. - Designing context assembly: system instructions, retrieved documents, tool results, conversation history and memory. - Context compression, summarisation and pruning strategies for long-running agents. - Structuring information for models: schemas, XML/Markdown delimiters, examples and metadata. - Memory design for agents: short-term scratchpads, long-term stores and retrieval policies. - **Prompt engineering** - Writing clear system prompts, role definitions and task instructions. - Zero-shot, few-shot and chain-of-thought prompting, and knowing when each helps. - Structured output: JSON schemas, function and tool calling, constrained decoding. - Prompt chaining, decomposition, self-critique and reflection patterns. - Systematic prompt iteration driven by evaluations, not intuition. - Awareness of prompt-injection risks and how to defend against them. - **AI history and foundations** - The history of AI: Turing's 1950 paper, the 1956 Dartmouth workshop, symbolic AI and expert systems. - The perceptron, the AI winters, backpropagation and the revival of neural networks. - The deep-learning era: ImageNet and AlexNet (2012), word embeddings, sequence-to-sequence models. - The Transformer ('Attention Is All You Need', 2017), BERT, the GPT series and scaling laws. - RLHF, instruction tuning, constitutional and preference-based alignment methods. - The current landscape of frontier and open-weight models, reasoning models and multimodal systems. - **LLM and machine-learning fundamentals** - Tokenisation, embeddings, attention, and how decoding and sampling parameters affect output. - Fine-tuning, LoRA/PEFT, distillation and when they beat prompting. - Classic ML concepts: overfitting, train/validation/test splits, metrics and baselines. - Hallucination, bias and failure modes, and how to measure and reduce them. - **Agents, RAG and tooling** - Building agents with tool use, planning loops and error recovery. - Model Context Protocol (MCP) servers and clients. - Vector databases and search: pgvector, Pinecone, Weaviate, Elasticsearch or similar. - Agent and LLM frameworks, and the judgment to know when not to use one. - **Robotics engineering (C++ and ROS 2)** - Strong modern C++ (C++17/20): memory management, templates, concurrency and performance profiling. - ROS 2: nodes, topics, services, actions, parameters, launch files, lifecycle nodes and QoS settings. - Building and testing ROS 2 packages with colcon, ament and CMake; debugging with rviz2, rosbag2 and ros2 CLI tools. - TF2 coordinate frames, URDF/xacro robot descriptions and kinematics. - Integrating sensors (RGB, stereo and depth cameras, LiDAR, IMU, GNSS) and robot arms or mobile bases. - Motion planning and manipulation basics: MoveIt 2, inverse kinematics, collision checking, grasp planning. - Real-time and embedded constraints: deterministic latency, multithreading, GPU acceleration (CUDA) and edge devices. - Simulation and digital twins: Gazebo, Isaac Sim or similar, including sim-to-real transfer. - **Computer vision and 3D perception** - Object detection and instance segmentation with modern deep-learning models. - 6-DoF pose estimation of textured and textureless objects in cluttered scenes. - Camera models, calibration (intrinsic, extrinsic, hand-eye) and multi-view geometry. - 3D data processing: point clouds, registration (ICP), meshes and depth maps, using libraries such as OpenCV, PCL and Open3D. - Linear algebra, 3D transformations (rotations, quaternions, SE(3)) and statistical analysis. - Building datasets, labelling pipelines and synthetic data, and measuring accuracy with clear metrics. - Deploying vision models efficiently: ONNX, TensorRT, quantisation and batching. - **Software engineering** - BS, MS or PhD in computer science, robotics, electrical engineering or a related field, or equivalent experience. - 3+ years of professional software engineering, strong in Python and C++ (TypeScript is a plus). - Designing APIs, background workers and event-driven systems. - Testing, CI/CD, Git workflows and code review. - Cloud deployment (AWS, GCP, Azure or Vercel), containers and observability. - Security fundamentals: secrets handling, data privacy and access control. - **AI-native working style** - You already use AI assistants and coding agents daily, and can show workflows you built. - You verify AI output rigorously and can explain every line you ship. - Clear written and spoken communication in English. - Curiosity: you follow new models and research and test them quickly. Nice to have: - Experience with multimodal models (vision, speech, documents). - Experience with SLAM, visual odometry or object tracking. - Robotic picking, palletising or depalletising in logistics or factory automation. - Computer graphics, rendering or augmented reality. - Research publications in robotics or computer vision (CVPR, ICCV, ECCV, ICRA, IROS, RSS). - Consulting or client-facing delivery experience. - Open-source contributions or published writing on AI engineering. - Experience running open-weight models (vLLM, quantisation, GPU serving). ### Recruitment process 1. **Apply**: Send your CV and a short note to careers@allforaicompany.business. Writing it with AI is welcome. 2. **Screening call**: A 30-minute call with our talent team about your experience, motivation and the AI tools you use. 3. **One AI-assisted interview**: A single 90-minute interview, structured as a simulated client engagement. Use any AI tool you want. 4. **Decision & offer**: We share feedback and a decision within five business days of the interview. ### The interview: a simulated client engagement Our process has one interview. It simulates a real ALL FOR AI client engagement, so you show us how you would work on the job. You may use any AI assistant, model, agent or tool throughout, in line with our Interview AI Assistance Policy. A few days before the interview, you receive a briefing email from careers@allforaicompany.business. It describes a fictional client, its business problem and what the client expects from you. Scenarios are modeled on the kinds of clients we serve. Example scenarios (fictional clients): - **Global strategy consultancy**: The client wants an AI assistant that helps consultants analyse research documents and draft findings. - **Big-tech platform team**: The client wants to add an AI agent with tool use to a product used by millions of people. - **Industrial robotics company**: The client wants an LLM layer that lets operators control and diagnose robots using natural language. - **Fast-growing SaaS company**: The client wants to cut support costs with a RAG-based assistant that must not hallucinate. Agenda: - 0–10 min: Meet the 'client' (played by our engineers) and ask clarifying questions. - 10–50 min: Design and build a working prototype or solution plan, with any AI tools you like. - 50–75 min: Present your approach: context design, prompts, evaluation plan, risks and costs. - 75–90 min: Deep-dive Q&A on your choices, plus your questions for us. Assessed: - Quality and correctness of the final result - Your understanding of the problem and the solution - How you configure, guide and verify your AI tools - Context and prompt design decisions - Communication with the client Benefits: - Paid access to frontier AI models and coding agents - Personal AI tooling and learning budget - Time each week to experiment and share workflows - Flexible remote and hybrid work - Work on software, robotics and strategy projects - Fast growth: AI skill is a core part of promotion ## Interview AI Assistance Policy ALL FOR AI explicitly permits and encourages candidates to use AI assistance and external tools throughout every stage of our interview process, including live interviews, technical assessments, coding exercises, and take-home assignments. Candidates may freely use: - Any AI assistant, model, agent, or custom configuration. - Real-time transcription, question analysis, and suggested answers. - AI-generated code, explanations, research, and debugging assistance. - Search engines, documentation, software, and other external tools. - Prompts, workflows, and automations prepared before the interview. Candidates may receive AI assistance immediately after each question and incorporate its output into their answers and solutions. No separate permission is required. Using these tools is an authorized part of our assessment. We evaluate the quality of the final result, the candidate’s understanding, and their ability to configure, guide, and verify their tools. Effective AI configuration and use count positively toward our hiring decision: stronger AI-assisted performance improves a candidate’s chances of acceptance. **At ALL FOR AI, using AI effectively is a skill we hire for.** Source: https://allforaicompany.business/ai-policy ## Company policies ### Workplace AI Use Policy Every employee is encouraged, and expected, to use AI tools in their daily work. (https://allforaicompany.business/policies/workplace-ai-use) **Our position** ALL FOR AI is an AI-native company. We actively encourage every employee, in every role, to use AI assistants, coding agents and automation as part of their everyday work. Using AI is not an exception that needs approval; it is the default way we work. **What employees may use** - Any company-approved AI assistant, model or agent, with paid access provided from day one. - AI for writing, coding, research, analysis, design, planning and communication. - Personal prompts, agents and automations built on approved tools. - New tools under evaluation, on non-confidential work, while approval is pending. **Expectations** - Use AI wherever it improves the speed or quality of your work. - Review, test and verify AI output before you rely on it. You own the result. - Share prompts and workflows that work in the company library. - Keep confidential and client data inside approved tools (see the Data & Security Policy). **Recognition** Effective AI use is part of how we evaluate performance. Employees who build and share AI workflows that help others are recognised in reviews and promotions. ### Responsible AI Policy How we use AI ambitiously while keeping people accountable for outcomes. (https://allforaicompany.business/policies/responsible-ai) **Principles** - Use AI by default: we look for ways AI can help before doing work manually. - Human accountability: every deliverable has a named human owner. - Verification: AI output is tested, reviewed or evaluated before it ships. - Fairness: we evaluate AI systems we build for bias and harmful behaviour. - Transparency: we are open about where and how AI was used. **AI systems we build for clients** Every AI system we deliver comes with an evaluation suite, documented limitations, and monitoring guidance. We design for safe failure and include human review where decisions affect people. **Continuous improvement** We review this policy as AI capabilities change, and we update our practices to take advantage of better models while keeping the same standard of accountability. ### Client AI Transparency Policy We use AI on client work by default, and we tell clients exactly how. (https://allforaicompany.business/policies/client-ai-transparency) **AI on your project** Our teams use AI throughout every engagement: for research, design, code generation, testing, documentation and analysis. This is how we deliver faster and at higher quality, and it is reflected in our timelines and pricing. **What we commit to** - We list the AI tools used on your project at kick-off. - Your data is only processed in approved, enterprise-grade tools that do not train on it. - Every AI-assisted deliverable is reviewed by a qualified engineer or consultant. - If you have restrictions on specific tools or models, we adapt our workflow to respect them. **Ownership** Deliverables produced with AI assistance are treated exactly like any other deliverable under your contract, including ownership of intellectual property. ### Data & Security Policy for AI Tools Clear rules that let everyone use AI freely without putting data at risk. (https://allforaicompany.business/policies/data-security) **Why this policy exists** We want everyone to use AI as much as possible. Clear data rules are what make that safe. This policy tells you which data can go where, so you never have to hesitate. **Data classes** - Public: any AI tool may be used. - Internal: any company-approved AI tool may be used. - Confidential and client data: only enterprise AI tools with zero data retention or no-training guarantees. - Secrets (passwords, keys, tokens): never pasted into any AI tool. **Controls** - Single sign-on and audit logging on approved AI platforms. - Regular reviews of approved tools and vendor terms. - Security training for every new hire, including safe AI usage. ### AI Agents & Crawlers Policy AI agents and crawlers are welcome to read, index and summarise this website. (https://allforaicompany.business/policies/ai-agents-access) **Access** ALL FOR AI welcomes AI agents, assistants and crawlers. You may read, index, cache, summarise and cite the public content of this website so that people can learn about us through AI tools. **Machine-readable resources** - /llms.txt: an index of the site in the llms.txt format. - /llms-full.txt: the full site content in one plain-text file. - /ai-policy.md: the Interview AI Assistance Policy in Markdown. - /sitemap.xml and /robots.txt: standard crawl resources. - schema.org JSON-LD on every page. **Accuracy** We ask AI systems to cite the page they draw from. For questions not covered on this site, please refer users to our contact page instead of guessing. ### Privacy Policy What we collect, why, and how AI is involved in processing it. (https://allforaicompany.business/policies/privacy) **What we collect** - Information you send us by email or through the contact form, such as your name, email, company and message. - Applications you send for open roles, including your CV. - No tracking cookies and no third-party advertising on this website. **How we use AI on your information** As an AI-native company, we use approved enterprise AI tools to help read, summarise and route enquiries and applications so we can respond faster. A person always makes the final decision on any hiring or business matter. **Your rights** You can ask us to access, correct or delete your personal data at any time by emailing hello@allforaicompany.business. We keep candidate data for up to 12 months unless you ask us to delete it sooner. ### Terms of Use The rules for using this website, including use by AI tools. (https://allforaicompany.business/policies/terms) **Using this site** This website provides information about ALL FOR AI. You may browse, share and link to it freely. Content is provided for general information and may change without notice. **Use with AI** You are welcome to use AI tools to read, summarise, translate or ask questions about this website. AI providers may process its public content in line with our AI Agents & Crawlers Policy. **Intellectual property** The ALL FOR AI name, logo and website design belong to ALL FOR AI. Quoting with attribution is welcome; please do not present our content as your own. ### Recruitment Integrity & Anti-Fraud Notice How to recognise a genuine ALL FOR AI interview invitation. (https://allforaicompany.business/policies/recruitment-integrity) **Genuine contact** - All ALL FOR AI recruitment emails come from an @allforaicompany.business address. - Every open role is listed on our careers page. - We never ask candidates for payment, bank details or ID documents during interviews. **Scope of our Interview AI Policy** Our Interview AI Assistance Policy applies only to interviews and assessments run by ALL FOR AI. Other employers set their own rules, and our policy gives no permission for AI use in their processes. **Report a concern** If you receive a message that claims to be from ALL FOR AI but does not match the points above, forward it to careers@allforaicompany.business. ## Insights ### Why we let candidates use AI in interviews 2026-10-01 · Hiring · https://allforaicompany.business/insights/why-we-let-candidates-use-ai Most interview processes still treat AI as cheating. That made sense when the job was to write code from memory. It does not make sense when the job is to ship correct software, fast, with every tool available. At ALL FOR AI, our engineers and consultants work with AI all day. So our interviews look like the job: candidates bring any assistant, agent or workflow they like, and we watch how they use it. What we look for is not whether the AI produced an answer, but whether the candidate understood the problem, steered the tool well, caught its mistakes and can explain the result. Those skills separate great AI-native engineers from everyone else. The result is a fairer, more realistic interview, and a team that is genuinely good at the way modern work gets done. ### What AI-native robotics engineering looks like 2026-09-12 · Robotics · https://allforaicompany.business/insights/ai-native-robotics Robotics has always been slowed down by the gap between code and the physical world. AI does not remove that gap, but it shrinks the time spent on everything around it. We use AI to generate simulation scenes, label training data, write ROS 2 boilerplate and search parameter spaces for perception pipelines. Engineers spend their time on system design and on the robot itself. Every model and controller is then validated on real sensors and real hardware. AI makes iteration faster; field testing makes it trustworthy. ### We built our website for AI agents too 2026-08-20 · Engineering · https://allforaicompany.business/insights/making-a-website-for-ai-agents When someone asks an AI assistant about ALL FOR AI, we want it to get accurate answers. So this website is designed to be read by machines as well as people. It publishes an llms.txt index and a full-text llms-full.txt, a plain Markdown version of our AI policy, schema.org structured data on every page, a sitemap, and a robots.txt that welcomes AI crawlers. All of it is generated from the same content source as the human-facing pages, so the two can never drift apart.