AI app development that solves a defined business problem
We add AI to apps where it measurably saves time or improves decisions. Every AI feature starts with a test set and a target, so you know how well it works before your customers see it.
Book a discovery callHow we decide if AI is the right tool
We start by writing down the task, a set of real examples, and what a correct answer looks like. If a simpler rule-based approach meets the target, we’ll tell you. If AI is needed, we compare model options on accuracy, latency, cost, and data handling before building.
Common AI features we build
Assistants grounded in your content
Chat and search that answer from your documents and data using retrieval, with sources shown to users.
Document and form processing
Extracting fields from invoices, claims, contracts, and intake forms, with human review for low-confidence results.
Recommendations and personalization
Ranking products, content, or next actions based on behavior and context.
On-device machine learning
Image, text, and sensor models that run on the phone for speed and privacy.
Evaluation and monitoring
Test sets, accuracy dashboards, and alerts when quality or cost drifts.
Guardrails and review
Input filtering, output checks, and clear escalation to a person when the model is unsure.
Technology stack
We stay model-agnostic and pick providers based on your data, accuracy, and cost requirements.
Model providers
- Hosted LLM APIs
- Open-weight models
- Cloud ML services
Retrieval
- PostgreSQL with pgvector
- Vector databases
- Embeddings
On-device
- Core ML
- LiteRT (TensorFlow Lite)
- ML Kit
Engineering
- Python
- Node.js
- Evaluation tooling
How a project runs, start to finish
Each stage has a clear output, so you always know what you’re paying for and what comes next.
Discovery
We map users, goals, risks, and must-have features, then agree on a written scope, milestones, and estimate before design starts.
UX and UI design
Wireframes and a clickable prototype let you test the flow with real users before a line of production code is written.
Build in sprints
Two-week sprints end with a working build you can install on your own phone, plus a short demo and a list of what’s next.
Testing and QA
Automated tests, device testing, accessibility checks, and security review run throughout the build, not just at the end.
Launch
We prepare store listings, handle App Store and Google Play submission, and set up analytics and crash reporting.
Support and growth
After release we monitor stability, ship OS updates, and plan the next features based on real usage data.
Cost and timeline factors
AI projects have two cost lines: building the feature and running it every day.
- Accuracy target and how much human review is acceptable
- Volume of requests and per-request model costs
- Amount and quality of training or reference data
- Privacy requirements, including where data may be processed
- Ongoing evaluation and model updates
Frequently asked questions
Will our data be used to train someone else’s model?
That depends on each provider’s terms and your account settings. We review the data handling terms with you and choose options that match your privacy requirements.
How do you measure whether the AI works?
We build a test set from real examples and track accuracy against it before launch and after every change.
Can AI be added to our existing app?
Usually, yes. Most AI features connect through your existing backend without rebuilding the app.
What if the AI gives a wrong answer?
We design for that: confidence thresholds, source citations, human review for high-stakes outputs, and easy ways for users to flag errors.
Related services and resources
Have a task you think AI could handle?
Bring a few real examples. We’ll tell you honestly whether AI is the right fit.
- info@tactionsoft.com
- +1 (307) 459-0850 · +1 (512) 299-0926
- Chicago, IL · Cheyenne, WY · Austin, TX · Sacramento, CA