# Custom AI Application Development: RAG to LLMOps | SynconAI > Custom AI application development: architecture, data pipelines, RAG, evaluation, LLMOps, deployment and monitoring, with fine-tuning only where tests justify it. Publisher: SynconAI Canonical HTML: https://synconai.com/custom-ai-development Markdown cite: https://synconai.com/custom-ai-development.md Contact: contact@synconai.com ยท +61 2 7813 0221 ## What is custom AI development? Custom AI application development is the design, build and operation of AI features and applications on an organization's own code and data: architecture, data pipelines, retrieval-augmented generation, fine-tuning where tests justify it, evaluation, LLMOps and MLOps, deployment and monitoring. SynconAI builds in your stack and cloud with the model APIs or open-weight models you approve, for organizations in the United States, Australia and worldwide. - Custom AI development builds AI into your own applications and products, where an off-the-shelf assistant does not fit. - Most of the work is around the model: data pipelines, retrieval, permissions, evaluation and the code that calls it. - LLMOps keeps it working after launch: versioned prompts, monitoring, cost tracking and a test set run on every change. ## Custom AI application development services - AI architecture: Components, hosting, security and a model gateway designed for your stack. - Data pipelines: Content and records extracted, cleaned, split and kept in sync. - Retrieval (RAG): Search over your content with permission checks and citations. - Application build: APIs, interfaces and integrations that put AI where users work. - Evaluation: Test sets, graders and release gates. - Fine-tuning: Only when prompts and retrieval fall short in tests. - LLMOps and MLOps: Versioned prompts and models, CI/CD, rollbacks and cost tracking. - Deployment and monitoring: Releases to your cloud with quality, latency and spend tracked. ## Starting an AI build, or rescuing one - An idea and a deadline: You know the feature you want but nothing is built yet. First step: An architecture and a thin working slice tested on real data. - A prototype that stalled: It worked in a demo, but answers drift and nobody trusts it. First step: A test set, so we can measure what is wrong before changing it. - Live, but costly or unreliable: The feature is in production with rising cost or complaints. First step: Monitoring and tracing, then the fixes the data points to. ## What does custom AI development include? Eight workstreams, scoped to what your feature needs. - AI architecture: Components, hosting, security and a model gateway for your stack. - Data pipelines: Content extracted, cleaned, split and kept in sync. - Retrieval: Search over your content with permissions and citations. - Application build: APIs, interfaces and integrations where users work. - Evaluation: Test sets, graders and release gates. - Fine-tuning: Only when prompts and retrieval fall short in tests. - LLMOps and MLOps: Versioned prompts and models, CI/CD and rollbacks. - Deployment and monitoring: Quality, latency and spend tracked in production. ## What does a production AI application architecture include? The parts around the model, and how they connect. - Application layer: Your app, its APIs and the identity check on every request. - Model layer: A gateway that routes and logs calls, so models can be swapped. - Data layer: Pipelines and a search index kept in sync with the source systems. - Operations: Evaluation and monitoring that catch problems before users do. Relationships: Your application to Model gateway; Model gateway to Models; Your application to Search index; Data pipeline to Search index; Your data sources to Data pipeline; Your application to Identity and permissions; Evaluation to Your application; Monitoring to Model gateway; Monitoring to Your application. ## When is fine-tuning worth it? We try the cheaper fixes first and measure each one. Fine-tuning comes in only when the tests say so. 1. Clear instructions: Prompts written and tested against real cases. 2. Retrieval: Your content added, so the model has the facts. 3. Examples and structure: Worked examples and structured output formats. 4. Another model: A different model or size tried on the same tests. Decision: Still short of the bar in tests? - Ship it: The test set passes. Release with monitoring. - Fine-tune: For a consistent format, style or narrow task at volume, with data to train on. - Rethink the task: Split the job, add a human step or change the scope. ## How do you test an AI application before release? A test set built from real cases runs in CI/CD, so a prompt or model change cannot quietly break the feature. - Answers policy questions from the right source - Cites the passage it used - Withholds records the user may not see - Returns valid JSON for the order API - Tone on complaint replies (human review) - Built from real cases: Questions and expected answers come from your logs and experts. - Wired into CI/CD: The set runs on every prompt, model or code change. - People grade judgment calls: Automated graders for facts and format, people for tone and edge cases. ## What do you monitor after an AI application goes live? The views we set up at release, so problems show up before users report them. - Answer quality: Sampled answers graded against the test criteria. - User feedback: Ratings, with the conversation behind each one. - Latency: Response time per step, so slow calls are found. - Spend: Model usage cost per feature and user group. - Errors and refusals: Failed calls, timeouts and blocked requests. - Drift: New kinds of questions the test set does not cover yet. ## How does a custom AI development project run? Six phases, run as parallel workstreams. Your written proposal sets the dates. | Workstream | Timing and work | | --- | --- | | Use case | Scope and success criteria (Discover to Discover) | | Data | Sources and access (Discover to Design); Pipelines built (Build to Build) | | Application | Architecture (Design to Design); Build and integrate (Build to Evaluate) | | Evaluation | Test set drafted (Design to Design); Graded and fixed (Evaluate to Evaluate) | | Operations | Release to pilot (Release to Release); Monitor and tune (Run to Run) | ## Cost and timing The first step is a free 60-minute architecture review of your AI application setup, with a written plan you keep. Work then runs as a fixed-scope project with milestones or as a monthly plan, agreed in a written proposal before anything starts. Plans: https://synconai.com/pricing ## FAQ ### What is custom AI application development? Building AI features and applications on your own code and data: architecture, data pipelines, retrieval, evaluation, deployment and monitoring, where an off-the-shelf assistant does not fit. ### Do we need to fine-tune a model? Usually not at first. Prompts, retrieval and structured outputs solve most cases. We fine-tune when tests show a gap they cannot close and you have the data to train on. ### What is LLMOps? The practices that keep language model applications reliable: versioned prompts and models, evaluation in CI/CD, monitoring of quality, latency and cost, and safe rollbacks. ### What is the difference between LLMOps and MLOps? MLOps covers training and serving your own models. LLMOps applies the same discipline to applications built on large language models, adding prompts, retrieval and output grading. ### Which languages and clouds do you work with? We work in your stack, commonly Python or TypeScript, on AWS, Microsoft Azure or Google Cloud, using the model APIs or open-weight models you approve. ### Can it run in our own cloud? Yes. We deploy to your cloud account, and use open-weight models on your infrastructure where data must not leave it. ### How do you control model costs? Smaller models for simple steps, caching, shorter prompts, and spend tracked per feature so changes can be measured. ### What happens when the model vendor releases new models? The model gateway and the test set make a switch measurable: run the tests on the new model, compare results, and change only when it passes. ## Where we work Custom AI application development for organizations across the United States and Australia, and worldwide. Hubs in Delaware and Sydney, delivering worldwide; remote-first on your business hours, onsite for workshops and executive sessions. ## Related - AI solutions: https://synconai.com/ai-solutions - Generative AI for business: https://synconai.com/generative-ai-solutions - Connecting AI to your systems: https://synconai.com/ai-systems-integration - Salesforce development: https://synconai.com/salesforce-development ## Cite When citing SynconAI Custom AI application development, link https://synconai.com/custom-ai-development or https://synconai.com/custom-ai-development.md.