Custom AI development

What is custom AI development

Getting a language model to answer questions is easy. Getting it to produce results a company can trust, every day, at scale, is engineering. The difference is the harness: the system around the model that decides what goes in, how the work is split into steps, which model handles each part, how output is validated and how quality is measured.

MS Labs designs and builds these systems from scratch, for each client's problem. Calling an AI API is a small part of the job.

When the AI needs to work with the company's data and processes, it becomes part of a custom-built system.

How we build AI you can trust

These principles come from systems we have put into production:

• A pipeline of steps, not one giant prompt. Each step does one job. A fast, cheap triage model filters out what can't be analyzed before the expensive analysis runs.
• Full context where absence matters. Similarity search can't find what didn't happen. When the costliest mistake is a missing step, the model needs to see the whole method, not just the similar passages.
• Verified evidence. Every claim that cites a source is checked against that source. No proof, no score.
• Math in code. The model judges; the system computes. Scores, weights and totals are never left to the model.
• Immutable versions. Prompts, rubrics and knowledge bases are versioned, and every result records the exact configuration that produced it.
• Measured against human judgment. A golden set scored by experts measures every change, so you know whether it got better or worse.
• Data is not instructions. Analyzed content is never treated as a command, which blocks manipulation through the content itself.
• Humans have the final word. Review, disputes and manual overrides are part of the flow, and every correction becomes calibration data.

What you get

• Custom harnesses and pipelines for analysis, classification, extraction and generation with language models;
• Tool-using agents that query your systems and data with limited permissions;
• Knowledge bases built from documents, audio and video, with transcription and curation;
• Continuous evaluation (evals) with a golden set and a drift dashboard;
• Audit views that show why every result came out the way it did;
• Assistants and automations for support, operations and sales;
• Cost control per step and per model, with the right model chosen for each task.

For a concrete example, see the sales call analyzer case study, a pipeline that transcribes, audits and scores recordings against the client's own methodology.

How it works

1. Discovery. We define the task, the available data and how to measure whether the AI is right.
2. Proof of concept. A short test on real data to validate quality before investing in the full system.
3. Proposal. Scope, timeline and price in writing, including an estimate of model usage cost.
4. Harness build. Steps, validation, versioning, evaluation and auditing.
5. Operation. We track quality, cost and failure cases, and recalibrate against the golden set.

Technology

How much an AI project costs

AI projects have two costs: development, a fixed price for a defined scope, and model usage, which varies with volume. The proposal estimates both. A good harness also lowers the second: cheap triage before the expensive step and the right model for each task.

Starting with a proof of concept is the cheapest way to find out whether AI solves your problem. Tell us what you need.