Skip the hype. We build chatbots that cut support load, models that forecast demand, and dashboards that turn scattered data into clear decisions. Every project is scoped around a number it has to move.
AI is only worth building when it changes a number you care about: hours saved, tickets deflected, forecasts you can trust. That is how we approach AI development services at Larkwell Systems. We start with the operational problem, prove the idea on your real data, and ship a working tool your team actually uses. No research projects. No demos that die in a slide deck.
Our senior engineers have shipped machine learning models, chatbots, and analytics pipelines for real operations, not lab conditions. That means we care about the unglamorous parts: clean data, security, monitoring, and a clear way to measure whether the system is paying for itself. If a simpler tool solves your problem, we will tell you. If AI is the right call, we will show you the math before you commit.
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Demand forecasting, churn prediction, document classification, pricing models. We train and tune models on your own data, validate them against a baseline, and deploy them into the tools your team already uses. You see accuracy numbers before anything goes live.
Chatbots and assistants built on frontier models from OpenAI and Anthropic, grounded in your docs, policies, and product data. They handle routine questions around the clock and hand off cleanly to humans when it matters. Fewer tickets, faster answers, no robotic dead ends.
One dashboard instead of six spreadsheets. We connect your sales, operations, and finance data into live dashboards built with tools like Power BI and Metabase, so decisions get made on current numbers instead of last month's export.
Competitor pricing, market listings, public records, lead data. We build reliable scraping pipelines that collect the data you need, on a schedule, and deliver it clean and structured. Everything we build respects site terms and applicable data regulations.
Four connected stages. Each one has to pass before the next begins — and you see the numbers at every gate.
A use-case audit pinpoints where AI actually saves money — and the number it must move.
A fixed-scope pilot on your real data, measured against today's baseline.
Accuracy targets, human review where it matters, and no black boxes.
Monitoring, retraining, and reporting that shows the ROI in plain numbers.
Not sure where AI fits? Start with a fixed-scope pilot on your real data — and a number it has to move.
Honest answers — including the ones about price. Tap a question to expand the reply.
Yes, when it targets a specific bottleneck. The wins we see most often are support chatbots that handle routine questions, forecasting that improves ordering and staffing decisions, and dashboards that replace manual reporting. Where AI is not useful is as a vague initiative. If we cannot tie a project to hours saved or revenue protected, we will say so before you spend anything.
Scope drives price, but typical US-market ranges for AI development services look like this: a custom chatbot or automation build usually runs $10,000 to $40,000, document and data pipelines $8,000 to $30,000, and custom machine learning models $15,000 to $50,000 or more depending on data complexity. We usually recommend starting with a small, fixed-scope pilot so you can verify results before committing to a larger build.
Less than most people assume. Chatbots can work from existing docs, help articles, and policies. Dashboards use the data already sitting in your systems. Custom prediction models do need decent history, often a year or two of transactions, but part of our discovery work is auditing what you have and telling you honestly whether it is enough. If it is not, we will show you how to start collecting it now.
Yes, if the system is built for it, and that is a design decision we make up front. Your data is never used to train public models, access is restricted and logged, and sensitive information can stay inside your own cloud environment. For regulated data we design around your compliance requirements, such as HIPAA or SOC 2 obligations, and we put data handling terms in writing.
We set the yardstick before we build. That means agreeing on a baseline, such as current ticket volume, hours spent on reporting, or forecast error, and then tracking the same numbers after launch. Every project ships with a simple dashboard or report showing that comparison. If a pilot cannot demonstrate movement on the metric we agreed on, you have real grounds to stop before investing further.
Free 30-minute consultation. Straight answers and a written estimate — no obligation.
