AI Product Engineering
Production-ready AI applications built for real business outcomes
Most AI demos look impressive in a slide deck or local terminal but fall apart in production due to hallucinations, unpredictable latency, and cost overruns. We build AI-first web and mobile products with reliable architectures, secure data pipelines, and measurable outcomes. We help you move past raw hype and build tools that actually work for your users.
Core Capabilities
What we actually build
LLM integration and development
Integrating large language models securely into web and mobile products. We set up reliable APIs, manage context length, optimize token usage, and ensure data privacy.
Copilots and intelligent agents
Building production copilots and agentic workflows that perform complex, multi-turn tasks. Systems that act on user intent and integrate with your existing databases.
Testing, evaluation and guardrails
Rigorous evaluation suites (Evals) and real-time safety guardrails to catch hallucinations, prevent prompt injections, and guarantee predictable production behavior.
Retrieval-based systems (RAG)
Building custom vector search, semantic search, and Retrieval-Augmented Generation (RAG) systems over complex, multi-format internal company data.
Multi-step agentic automation
Connecting LLMs with business systems like CRM, ERP, and payment APIs for robust, autonomous end-to-end task execution without manual handoffs.
Target Audience
Who this is actually for
We partner with founders and product leads who need to build high-quality AI features without compromising on data privacy, system reliability, or user experience. Read more on our blog.
Founders wary of AI hype
Teams who want to add real value, secure data pipelines, and clear user experiences rather than building simple wrappers that stall in staging.
Teams solving specific bottlenecks
Operations teams looking to automate repetitive support volume, document parsing, CRM upkeep, or internal lookup tasks.
Engineering pods looking for depth
Organizations requiring experienced AI engineers to implement robust model evaluation frameworks, fallback layers, and vector storage.
Methodology
How we work
Discover
We identify the specific workflow bottleneck or product feature where AI can actually deliver measurable improvement.
Prototype
Rapid prototyping with a validation harness to test latency, prompt options, and model responses before full engineering.
Build
Production-grade implementation with custom evaluation pipelines, safety guardrails, and clean frontend UI integration.
Scale
Ongoing logging, analytics tracking, and performance tuning based on real user interactions to refine prompts and models.
Portfolio & Focus
Expanding our AI product engineering portfolio
Our approach to AI and LLM software quality
DEFX is actively designing, evaluating, and launching bespoke AI features and agentic automations for clients across regions. By focusing on production safety, guardrails, and deterministic testing rather than just raw wrappers, we ensure our clients build lasting technical assets.
Clear Answers
Questions people usually ask
No. We design and build complete AI systems. This includes custom data preprocessing, Retrieval-Augmented Generation (RAG) pipelines, evaluation suites (Evals) to measure accuracy, fallback logic, and deep integration with your company's existing APIs and databases.
We build with strict data boundaries. We can set up integrations utilizing secure enterprise API agreements where data isn't used for training, implement local open-source models, or work entirely within your private cloud environment to ensure compliance.
Reliability is our primary focus. We implement strict input/output guardrail layers, use multi-step validation agents to check outputs, and establish automated unit testing (Evals) across dozens of scenarios to guarantee performance bounds before deployment.
We are model-agnostic. We select the best tools for your latency, cost, and task constraints. This includes OpenAI's models, Anthropic's Claude, Google's Gemini, or fine-tuned open-source models like Llama running on optimized hosts.
What We Actually Use
The stack behind the AI systems we build
We are model-agnostic on purpose — OpenAI, Anthropic's Claude, Google's Gemini, or a fine-tuned open-source model like Llama, chosen for your latency, cost, and data constraints rather than defaulted to one vendor. Python and Node.js for the application layer, vector stores for RAG pipelines, and AWS or Google Cloud for hosting — with everything sitting behind the evaluation and guardrail layers described above before it ever reaches production.
Scope, Honestly
What this looks like in practice
Most AI feature builds we take on land between $10k and $50k for a first production release, with agentic or multi-system automation work running higher depending on how many internal systems it needs to touch. A short discovery call is how we turn that into a real number for your use case. What we don't do: ship a demo that only works on the happy path, skip an evaluation suite to hit a deadline, or plug in a model without a plan for what happens when it's wrong.