AI Mobile App Development Company
FISTA Solutions builds AI-native mobile apps: in-app assistants scoped to the user's data, on-device inference for privacy and offline use, multimodal capture through camera and voice, and personalization — each with an evaluation harness, cost model per active user, and a working path when the model is unavailable.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does AI mobile app development include?
AI mobile engagements deliver the feature and the machinery around it: prompt and model architecture, on-device versus cloud decisions, streaming interfaces that feel fast, evaluation harnesses, cost controls, safety filtering, and fallback paths when inference fails.
- 01
In-app assistant
A scoped assistant answering from the user's own data with the app's permission model enforced.
Assistant - 02
On-device inference
Local models for privacy-sensitive or offline features, sized for the device range you support.
On-device - 03
Multimodal capture
Camera and voice input turned into structured data, removing typing on a small keyboard.
Input - 04
Streaming interfaces
Token streaming and optimistic states so AI features feel immediate rather than stalled.
UX - 05
Evaluation and cost control
Golden sets per feature, per-user budgets, caching, and model routing to keep quality and margin stable.
Operations
Requirements
Which requirements shape AI mobile app development?
AI on mobile adds constraints that server-side AI does not have: latency is felt immediately, connectivity is unreliable, per-user cost scales with engagement, and app stores apply their own rules to generated content and data handling.
| Requirement | Why it matters | How FISTA builds to it |
|---|---|---|
| Perceived latency | Users abandon features that feel slow. | Streaming responses, optimistic UI, prefetching, and measured time-to-first-token budgets per feature. |
| Offline behavior | Connectivity is not guaranteed. | On-device fallbacks or a clear degraded path, so the app stays usable without inference. |
| Per-user cost | Engagement drives inference spend. | Cost modeled per active user, with routing, caching, context limits, and budgets enforced server-side. |
| Store policy | Stores regulate generated content and data use. | Content safety filtering, age-appropriate handling, and accurate privacy declarations for model data flows. |
| Quality drift | Model updates change behavior silently. | Golden-set evaluation in CI with alerts, so provider changes surface before users find them. |
Where AI fits
Where does AI fit in AI mobile app development?
The first AI feature should remove a concrete friction: typing, searching, or understanding. Choose a moment where the user currently works hard, measure it, and ship one feature well rather than five that each work approximately.
- 01
1. Find the friction
Where users type most, search hardest, or abandon most often is where AI earns its place.
- 02
2. Choose on-device or cloud
Privacy, latency, and cost decide, and the decision is recorded with its trade-offs.
- 03
3. Build the evaluation set
Real inputs with expected behavior, so quality is measured before and after every change.
- 04
4. Design for failure
Streaming, timeouts, and a working fallback path when inference is slow or unavailable.
- 05
5. Model the cost
Per-active-user cost before launch, so pricing and limits are decisions rather than reactions.
Cost and timeline
How much does AI mobile app development cost, and how long does it take?
Cost is driven by feature scope, on-device model work, and evaluation depth, plus ongoing inference spend; timeline by evaluation and store review. FISTA does not quote blind: the scoping call returns a feature design, a cost model, and a phased estimate.
Inference is an operating cost that scales with engagement, which makes it a product decision rather than an infrastructure detail. FISTA models per-user cost during design so pricing, limits, and packaging are set with evidence.
On-device models trade inference cost for engineering cost and app size. That trade is worth making for privacy-sensitive or offline features and rarely worth it otherwise, and the analysis is done explicitly.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver a mobile app?
FISTA delivers apps in four phases: product discovery that produces the MVP specification and clickable flows, architecture for app and backend with the store-compliance checklist, two-week builds demoed on TestFlight and Play internal tracks, and a monitored release with analytics, crash reporting, and a post-launch iteration plan.
- 1
Discover the product
User and business goals, competitive review, feature prioritization, and a written MVP specification with acceptance criteria.
OutputMVP spec, user flows, estimate
- 2
Design app and backend
Platform decision, architecture, data model, API contracts, design system, and the store-compliance checklist.
OutputArchitecture, API contracts, designs
- 3
Build in sprints
Two-week increments shipped to TestFlight and Play internal testing, with automated UI and API tests.
OutputTestable builds every sprint
- 4
Launch and iterate
Store submission, monitoring, crash and analytics dashboards, and a prioritized post-launch backlog.
OutputLive app, dashboards, roadmap
Why FISTA
Why choose FISTA Solutions for AI mobile app development?
FISTA builds mobile AI features with evaluation harnesses, per-user cost models, and fallbacks that keep the app working when inference does not. FISTA is an official Anthropic partner with production experience across model providers.
AI Apps specifics
- Every AI feature ships with a golden-set evaluation running in CI, so provider updates cannot silently degrade it.
- Per-active-user cost is modeled before launch, with routing, caching, and budgets enforced server-side.
- Streaming and optimistic interfaces make features feel immediate, with measured time-to-first-token budgets.
- Every feature has a working path when inference is slow or unavailable, rather than a spinner.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What app buyers ask before they commit.
Straightforward guidance for evaluating scope, fit, and the next step.
01Should AI run on the device or in the cloud?
On-device for privacy-sensitive or offline features and low-latency perception tasks; cloud for heavier reasoning and quality. The decision is made per feature against privacy, latency, quality, and cost, and recorded with its trade-offs.
02How do we control AI costs in a mobile app?
By modeling per-active-user cost before launch and enforcing routing, caching, context limits, and budgets server-side. Client-side limits alone do not hold once engagement grows.
03What happens when the model is slow or down?
The feature degrades to a working path — cached results, a non-AI flow, or a clear state — rather than a spinner. That fallback is designed in from the start.
04Do app stores restrict AI features?
They apply rules on generated content, age appropriateness, and data handling. FISTA implements content safety filtering and accurate privacy declarations covering model data flows during the build.
05Can you add AI to our existing app?
Yes, starting with an assessment of the data model and permission layer, then one well-scoped feature with evaluation and cost control rather than a broad AI layer.
Scoped in writing before you commit
Add the AI feature users will actually keep using.
Bring the friction you want removed. The scoping call returns a feature design, an evaluation plan, a cost model, and an estimate.