AI Software Development Company

Plenty of agencies added “AI” to their homepage last year. We added it to our products: LLM-powered test generation, enterprise knowledge assistants with cited answers, voice-driven AI consultations — all shipped, all documented in our case studies. We build AI that survives contact with production.

Discuss Your AI Project

AI That Does a Job, Not a Demo

The gap between an impressive AI demo and a dependable AI product is where most projects die. A demo answers the easy questions in front of an audience. A product handles the messy PDF, the ambiguous request, the user who types in another language, and the day the model API has an outage — without embarrassing you. Crossing that gap is engineering, not prompting, and it is what we actually sell.

Our AI work grew out of two decades of building business software, and it shows in the details: we care about latency budgets, cost per request, fallbacks when the model fails, and what happens to a wrong answer downstream. The AI products in our case studies — an EdTech engine that turns uploaded study material into structured practice tests, an enterprise assistant that cites its sources, a multilingual voice consultation platform — run for real users, not conference slides.

What We Build

LLM Integration

Large language models wired into your product or workflow: chat over your knowledge base with cited sources, drafting, summarization and extraction, with retrieval architectures that keep answers grounded in your data.

AI Agents & Workflow Automation

Agents that complete multi-step work across your systems, with permissions, audit trails and human approval where it counts. Full details on our AI automation services page.

Document Intelligence & OCR

Pipelines that read what humans used to retype: scanned documents, photos, handwritten forms. Our EdTech case study turns raw uploads into structured, validated output at scale.

Predictive Analytics

Forecasting and scoring models built on your operational data, integrated where decisions happen rather than trapped in a dashboard nobody opens.

AI in Mobile Apps

Voice interfaces, on-device scanning, AI chat with proper mobile UX. Built with our React Native team, so the AI and the app are one project, not two vendors.

Custom & Fine-Tuned Models

When prompting is not enough: fine-tuning on your proprietary data for accuracy, tone or cost, and self-hosted open-source models where privacy demands it.

Where AI Pays Off — and Where It Doesn’t

We turn down AI work more often than you would expect, because a project that should not exist helps nobody. AI earns its cost where a task is high-volume, language- or document-heavy, and tolerant of review: support triage, document processing, knowledge search, drafting, classification. It disappoints where the task is rare, already fast, or demands perfect accuracy with no human check. If a spreadsheet formula solves your problem, we will say so and save you the budget.

The other honest warning: the model is maybe a fifth of the work. The rest is data preparation, integration with your systems, evaluation on real cases, guardrails and monitoring. Vendors who skip those parts produce the demos that never ship. It is also why we insist on starting with a pilot on your actual data instead of a contract for a platform.

How an AI Project Runs

Discovery first: a short, focused look at your workflows to find where AI has measurable value, ending in a written recommendation — including “don’t do this” when that is the right answer. Then a pilot: three to six weeks to a working system on your real data, with accuracy measured against cases your team has already handled. Production comes only after the pilot proves itself: integration, monitoring, cost controls and a rollout your team actually adopts. You are never asked to fund a moonshot on faith.

  • Shipped AI products documented in public case studies, from OCR pipelines to cited-answer assistants.
  • Product engineering roots: 21+ years building software people rely on daily.
  • Model-agnostic architecture: benchmarked per task, swappable by design.
  • Pilot-first engagements: weeks to a verdict on your own data, not months to a promise.
  • Privacy by architecture: retrieval scoping, data masking, per-user access controls.
  • Honest running costs: per-request economics estimated before you build.

Wondering What AI Can Do for Your Business?

Bring us one painful process. We will tell you honestly whether AI is the answer.

Get Free Consultation

Frequently Asked Questions

How much does AI software development cost?

Less than most companies fear, if you start small. A working pilot on your own data typically takes 3 to 6 weeks. Production builds vary with integration depth, and running costs matter as much as build costs: model API pricing, hosting and monitoring. We estimate both before you commit.

Which AI model should we use — OpenAI, Anthropic, or open source?

The unpopular truth: for most business tasks, several models are good enough, and the architecture around the model matters more than the logo on it. We build so the model is swappable, benchmark on your actual data, and choose per task.

Is our company data safe when using LLMs?

It can be, with the right setup. Enterprise API tiers do not train on your data. Beyond that, we minimize what leaves your systems: retrieval sends only relevant snippets, sensitive fields are masked before any API call, and access is scoped per user.

What is the difference between an AI chatbot and an AI agent?

A chatbot answers questions. An agent does work: it looks things up, calls your systems, creates records and completes multi-step tasks. Agents are more valuable and more dangerous, which is why we build them with permissions, audit logs and human approval for consequential actions.

Do you build AI features into existing apps or only new products?

Both. A large share of our AI work is adding capabilities to products that already exist: document scanning in a field app, an assistant inside an enterprise tool, automated processing behind an existing workflow. Our app teams and AI work are one organization, so integration is unusually smooth.

How do we start an AI project without wasting budget?

Start with one process, not a platform. A short discovery finds the workflow with clear, measurable value; a pilot proves it in weeks on your real data; results decide whether to expand. If AI is the wrong tool for your problem, we will tell you before you spend.

Let’s talk