AI Development Services

AI Development, One Tested Sprint at a Time

SprintOne Labs builds AI features in two-week sprints and provides AI development services to startups, SaaS companies and agencies: generative AI and LLM apps, AI agents, machine learning models, and AI features inside existing products. Every sprint closes with a working increment, and every build passes its tests before anyone demos it. Our AI software development services, sometimes searched as artificial intelligence development services, move from proof of concept to MVP to production. Teams with their own developers can take custom AI development services as a team extension instead.

What Are AI Development Services?

AI development services are the design, build, integration and maintenance of custom AI solutions for one specific business. Typical outputs include generative AI applications, machine learning models, AI agents and chatbots. The provider brings the engineers, the data work, the evaluation and the upkeep after launch, so the solution keeps working as inputs change.

The term gets stretched, so here is what it does not cover:

• A SaaS subscription. Buying an AI tool gives you a seat. It does not give you a feature inside your own product.
• A prompt-only wrapper. One prompt around a public API is a demo. It has no retrieval, no evaluation set and no fallback.
• A slide deck. Strategy without a running increment proves nothing about your data.
• A one-time handover. Models drift and providers change versions. Maintenance belongs to the service.

Real AI software development solutions include four layers. Data comes first: what exists, how clean it is, who may use it. The model layer follows: an API model, a fine-tuned one, or a custom one. Then comes the software that wraps the model: screens, access rules and logs. Evaluation is the last layer, and it never stops.

We treat all four as engineering work on a sprint board. Each layer gets tickets, owners and tests. That keeps an AI project as visible as any other part of your roadmap.

What We Build: From LLM Apps to AI Agents

Seven cards describe the AI work we take on. A single card can fill one sprint goal, and a typical backlog mixes several.

Generative AI & LLM Applications

Our generative AI development services cover retrieval-augmented generation (RAG), fine-tuning and prompt engineering. We pick the lightest technique that meets the target. Large language models (LLM) get grounded in your documents instead of guessing.

Ships as: a RAG pipeline over your content, plus a versioned prompt library.

AI Agents & Agentic Workflows

AI agent development services turn a multi-step task into a tool-using agent. Each tool call is logged and limited by permissions. Agentic workflows stop and ask a human when confidence drops.

Ships as: an agent with a defined tool list and a step-by-step trace log.

AI Chatbots & Assistants

An AI chatbot answers from your knowledge base and hands off to a person when it should. Tone, scope and refusal rules live in configuration, not in a hidden prompt.

Ships as: an in-product assistant and a handoff rule set.

Machine Learning & Predictive Models

Classic machine learning still wins on structured data. We train models for predictive analytics, computer vision and natural language processing (NLP) tasks such as classification and extraction.

Ships as: a trained model behind an API, with a retraining script.

AI Integration Into Existing Products

AI integration adds a model-backed feature to the app you already run. Our engineers work in your repository beside your developers. AI application development services often begin here.

Ships as: a feature flag, an endpoint and the tests around both.

AI Testing & Evaluation

Evaluation probes an AI feature for hallucination, weak robustness and bad failure modes. It can cover a system we built or one another team built. Guardrails are tuned from the findings.

Ships as: an evaluation set and a pass/fail report per build.

AI Consulting & Use-Case Validation

AI consulting and strategy work answers one narrow thing first: whether the use case is worth a PoC. A data audit shows what the model can learn from. Weak ideas end here, before budget is spent.

Ships as: a feasibility note and a ranked use-case list.

Choosing between techniques is part of the job. Prompt engineering comes first because it is the fastest to test. RAG follows when the model needs facts it was never trained on. Fine-tuning is the last step, used when tone, format or cost per request still misses the target. Each move up that ladder must earn its place on the evaluation set. A technique that does not raise the score does not enter the backlog.

Every card ends in running software. A feasibility note is the only paper deliverable, and it exists to protect the sprints after it. We do not sell research without an increment attached.

Two of these cards will get their own pages later. Until then, the scope of LLM development services and AI product development services is agreed per backlog. Custom AI development rarely fits one card anyway.

Our Sprint-Based AI Delivery Process

Delivery follows a calendar, not a phase chart. Every entry below ends with something you can run. As a custom AI development company, we plan the model work and the test work on the same board.

1. Sprint 0 — Discovery and feasibility. We define the use case and run a data audit. Success metrics get named and written down. A small spike confirms that a model can do the task at all.
2. Sprint 1 — Acceptance criteria and first slice. Both sides sign what "done" means and how it is measured. The first evaluation set is drafted from real user journeys. One thin end-to-end slice goes live in a test environment.
3. PoC sprints — Prove the core. A proof of concept typically takes 1–3 months. Each sprint adds one capability and reruns the evaluation set. You see scores move at every demo.
4. MVP sprints — Build around the model. Interface, permissions, logging and cost limits are added. QA engineers work the same stories as developers. Autotests and model evaluations run on every build.
5. Release sprint — Evaluate and ship. The full scenario suite runs against the release candidate. Results are compared with the signed criteria. Rollout goes behind a flag, to a small audience first.
6. Sprint N — Monitor, retrain, support. MLOps takes over: model monitoring and retraining, drift alerts, version pinning. Production failures become new evaluation cases. The backlog then continues with the next feature.

A SaaS team releasing every two weeks can map this calendar straight onto its own. Our sprint review becomes one item in your review. Your product owner accepts AI stories the same way as any other story.

Everything built in those sprints belongs to you. That includes the code, the prompts, the evaluation sets and any fine-tuned model. Repositories sit under your account from Sprint 0. Discovery itself starts under an NDA.

Two habits keep the calendar honest. A sprint goal is always a behavior a user can see. A story is not closed until its evaluation cases pass.

AI Testing on Every Build

Models give different answers to the same input. Ordinary unit tests miss that, so we test AI features on real user scenarios before your users see them. Every commit, prompt edit and model upgrade triggers the run.

The suite checks six things:

• Response stability across repeated runs of one input
• Prompt injection and other malicious input
• Incorrect, empty or oversized input
• Latency and cost under load
• Behavior when the model API fails or times out
• Bias and compliance rules, where your domain needs them

Scenario suites come from actual user journeys, not invented prompts. A golden dataset holds inputs with approved answers. Regression against that dataset reruns whenever a prompt or model version moves. Edge cases that a script cannot judge go to human review.

Consider a new AI assistant added to an e-commerce checkout before peak season. The suite replays real cart questions at peak load. It also cuts the model connection mid-conversation. You learn how the assistant fails while the stakes are still low.

Guardrails are tested like features. A rule that blocks out-of-scope answers has its own test cases. So does the fallback message shown when retrieval finds nothing. Hallucination is tracked as a failed case in the golden dataset, not as an anecdote from a demo. AI-assisted test generation helps us draft scenario variants faster. No drafted case enters the suite without an engineer's approval.

Testing does not end at launch. Production traffic is sampled and scored against the same cases. A drop in the score opens a ticket in the next sprint. New failure patterns are added to the golden dataset, so the suite grows with real use.

Each run produces an evaluation report with pass/fail against the agreed acceptance criteria. The wider record matters too: critical defects found before release stand at >97%. An AI development services company should show you that report without being asked.

AI Development Cost by Stage

Budget follows the stage you are in. The table shows typical US market ranges, not our quotes.

Our own pricing reads best per sprint. Here is a two-week AI sprint at the rates we have confirmed for US clients:

• Developer, full time: 80 hours at $65–75/h
• QA at half allocation: 40 hours at $40–45/h
• Sprint total: roughly $6,800–$7,800

An automation engineer for the evaluation pipeline bills at $45–55/h. A dedicated AI/ML engineer bills at $70–80/h. No minimum project size exists, so a PoC can be a handful of sprints. Seniority, stack and time-zone overlap set the final figure for each engineer.

Model usage fees are a separate line from engineering time. We measure cost per request during the PoC, so that line is known before traffic grows. A prompt that doubles token use shows up in the sprint report, not on a surprise invoice.

Planning works backward from the stage. A PoC backlog is cut into sprint goals, and each goal gets an evaluation target. The estimate you receive lists those sprints, the roles in each and the rate band per role. Scope changes move a sprint in or out; they do not reopen the whole budget.

Four drivers move an AI budget more than headcount does:

• Data readiness. Clean, labeled, accessible data shortens every sprint.
• Model choice. Calling a hosted API costs less at the start than fine-tuning or training a custom model.
• Integration depth. A standalone tool is simpler than a feature wired into billing and permissions.
• Evaluation depth. A regulated workflow needs a larger scenario suite.

A range is easy for any AI software development company to give. A sprint count tied to a backlog is more useful, and that is what we send.

Get a scoped PoC estimate

Ways to Work With Us

Three formats are on offer, and none of them changes the two-week rhythm.

Project-Based PoC or MVP

Scope is fixed, and acceptance criteria are written before the first sprint. We own the backlog, the model work and the evaluation. You attend the demo every two weeks and accept or reject the increment. This format fits a first AI feature with a clear goal.

Dedicated AI Team or Team Extension

AI/ML engineers, developers and QA join your sprints and your tools. The first engineer starts in 3–5 business days. Capacity goes up before a launch and down after it, with the original contract left as it is. The full terms sit on our staff augmentation page.

AI Added to an Existing Product

Your developers keep the codebase. Our engineers bring the retrieval, prompt and evaluation work into it. Integration and testing happen in your pipeline, under your review rules. Teams comparing an AI development services company with hiring in-house often begin with this format.

Each format uses the same reporting. A sprint report lists what shipped, which evaluation cases passed and what the model cost to run. Questions go to the engineers directly, in the channel you already use.

Onboarding is short in every case. We need the repository, a staging environment and one product contact. The first sprint goal is agreed on the kickoff call.

Switching between formats is common. A PoC that proves itself usually becomes a dedicated team. The signed criteria and the evaluation set carry over unchanged.

Why SprintOne Labs

Fast AI delivery is worth little unless each increment holds. Four practices make that true for the AI work we ship.

QA in Every AI Sprint

A working, tested increment closes every sprint. QA engineers share the sprint with the people writing prompts and pipelines, and checks fire on every build. That is how >97% of critical defects get caught before release.

AI Tested Before Release

Real user scenarios drive our evaluation: unstable answers, hostile input, provider outages. The scenario design is reviewed by our ISTQB-certified engineer. Your users never serve as the first evaluators.

Criteria Signed Ahead of the Model Work

"Done" is defined in writing ahead of the first commit. An AI software development company that bills hours has no reason to stop tuning. We stop when the criteria pass.

Model Engineers, One Message Away

One shared channel connects you with whoever builds and tests your model pipeline. A custom AI development company with layers of managers loses detail. Here, the person who saw the failing case explains it.

Models, Frameworks & Infrastructure We Use

Most of our product work runs on a short, proven list. Models: OpenAI and Anthropic APIs, plus open-weight models when data must stay in your cloud. Frameworks: Python, LangChain and a vector database for retrieval. Infrastructure: AWS, Azure or GCP with Docker and Kubernetes.

The "every build" toolchain is just as short. The interface around the model is exercised with Playwright + TypeScript and Cypress. GitHub Actions runs each pipeline in Docker. Allure collects results, including evaluation scores, in one report.

Model choice stays open until the PoC data is in. LLM development services that lock a vendor on day one remove your best cost lever. We keep the model behind an interface so it can be swapped.

Your existing stack shapes the rest. Front ends are usually React or Next.js with TypeScript. Back ends are Node.js, .NET or Python. Terraform describes the infrastructure, so an AI environment can be rebuilt from code instead of from memory.

Free QA Audit in 2–3 business days

Before any AI sprint is planned, we can check the release cycle the feature will live in. The audit is free. It applies to teams that already ship AI and teams about to start.

What we check in your AI release cycle:

• The QA process, traced from a prompt change to production
• Test coverage on critical flows that touch a model, agent or retrieval step
• What is automated today and how well it works
• Where evaluation is missing or manual
• Which evaluation runs to automate next
• Gaps and risks in the current setup

The output is a brief written report: what we found, where the risk sits, what we recommend. Send access and product details, and the report follows in 2–3 business days. Nothing obliges you to continue. Many teams use the findings as the Sprint 0 input for their own AI roadmap.

The audit is not a sales document. It names concrete gaps, such as a prompt edited in production with no regression run. It also names what already works, so you do not rebuild it.

Access can be read-only. A staging environment, the repository and the current test assets are enough. If prompts live outside the repository, send those too.

FAQ

What are AI development services?

AI development services are engineering work that turns a business problem into a running AI system. The work spans data preparation, model selection, application code, evaluation and maintenance. At SprintOne Labs it runs in two-week sprints, each closing with a tested increment. Typical results are LLM applications, agents, assistants and predictive models inside your product.

How much do AI development services cost?

Cost depends on the stage and on data readiness. Typical market ranges are $5k–$50k for a PoC, $20k–$200k for an MVP and $50k+ for production systems. We price by sprint instead: a two-week sprint with a developer and QA at half allocation runs $6,800–$7,800 in our example. AI/ML engineers bill at $70–80/h.

How long does it take to build an AI solution?

A proof of concept usually takes 1–3 months. An MVP typically needs 3–6 months, and a production or enterprise system 6–12 months. Those are market norms, not promises. Our calendar shortens the feedback loop: you see a running increment every two weeks, so a wrong direction costs one sprint instead of a quarter.

Can you add AI features to our existing product?

Yes, adding AI to a live product is one of our three formats. Our engineers work in your repository and pipeline next to your developers. The feature ships behind a flag with its own evaluation set. Your team keeps ownership of the codebase, the prompts and every test asset.

How do you make sure the AI doesn't hallucinate or break in production?

No team can promise zero hallucinations, so we measure and contain them. Retrieval grounds answers in your documents. Guardrails block out-of-scope replies. A golden dataset reruns on every model or prompt change. Failure tests cut the provider connection on purpose. After launch, monitoring flags drift, and each production failure becomes a regression case.

Do you build with OpenAI/Anthropic APIs or custom models?

Both, and the PoC decides. Most projects start on OpenAI or Anthropic APIs because that is the fastest path to a measurable result. Fine-tuning or open-weight models come in when cost, latency or data residency requires them. The model sits behind an interface, so changing it later does not rewrite the application.

Can your AI engineers join our team instead of a separate project?

Yes, team extension is a standard option. The wait is 3–5 business days before AI/ML engineers sit in your sprints, tools and ceremonies. Your lead sets their priorities, and their commits land in your repository. You can scale the group up or down as the roadmap changes, with no contract to re-sign.