Home/Mobile App Development/AI / ML-Based Apps
Mobile App Development

AI / ML-Based Apps.
AI-native mobile apps.

Mobile + desktop apps with AI baked into the critical path — on-device LLMs, voice, vision, agentic actions, all wired into offline-first sync and the kind of polish that ships.

<300ms
On-device latency
Local
Privacy
200+
Eval cases
−41%
Cost-per-task
Trusted by enterprises
Aaj Tak
Times of India
BJP
Beyond Reach Premiere League
Red Fm
Wellness Fuel
Junior Cricket Championship
ON Energy
ORYZO AI
Nursing Sarathi
Baatshala Ai
The Traffic People
Aaj Tak
Times of India
BJP
Beyond Reach Premiere League
Red Fm
Wellness Fuel
Junior Cricket Championship
ON Energy
ORYZO AI
Nursing Sarathi
Baatshala Ai
The Traffic People
Aaj Tak
Times of India
BJP
Beyond Reach Premiere League
Red Fm
Wellness Fuel
Junior Cricket Championship
ON Energy
ORYZO AI
Nursing Sarathi
Baatshala Ai
The Traffic People
Service overview

Engineering ai / ml-based apps

We build mobile and desktop apps with AI in the critical path — not as a chat widget bolted on. On-device LLMs, voice, vision, agentic actions with confirmations. Phi-3, Gemma, Llama-edge running locally for low-latency, privacy-sensitive cases.

We architect for the realities: model fallback, partial-connectivity sync, citations, audit trail.

Practice signalsSenior-ledWeekly demosCode review on every PRProduction on-call
01
<300ms
On-device latency
Phi-3 / Gemma
02
Local
Privacy
No PII in cloud
03
200+
Eval cases
Per agent
04
−41%
Cost-per-task
v. cloud-only
Business challenges we solve

What keeps teams shipping ai / ml-based apps.

Latency on cloud LLMs killing the UX

Privacy-sensitive use cases that can't phone home

Hallucinations in production

Offline behavior breaking AI features

Billing surprise from LLM calls scaling

Agentic actions without confirmations in production

Why choose this service

Three senior-led practice lines.

Outcome-anchored, owned by a practice lead with clear accountability, weekly demos, and the kind of code review culture you'd build internally if you had a year.

01

On-device AI

Phi-3, Gemma, Llama-edge locally for low-latency and privacy.

02

Cloud + on-device

Online when available, graceful fallback offline.

03

Eval harness

200+ graded examples, regression in CI.

Key features

What ships, by default in every engagement.

F·01

On-device LLM

Phi-3 / Gemma / Llama-edge, GPU-accelerated.

F·02

Voice input

Speech-to-text, OS-level intents, latency budget.

F·03

Vision

On-device image segmentation, OCR, document understanding.

F·04

Agentic actions

In-app agent that takes real actions, with confirmation.

F·05

Citations

Above-threshold confidence requires citation.

F·06

Eval harness

200+ graded examples, regression in CI before merge.

Benefits & business outcomes

Numbers senior clients measure.

Latency

On-device for fast paths

<300ms
Outcome · 01

Privacy

On-device for sensitive paths

Local-first
Outcome · 02

Eval harness

Graded examples in CI

200+ cases
Outcome · 03
Development process

Senior squads. Tight loops. Code every day.

01
Week 1

Use-case triage

Map use cases to on-device vs cloud, eval criteria.

02
Week 2

Eval harness

200+ graded examples, success criteria.

03
Week 3–10

Build

Feature sprints, eval-driven, weekly release.

04
Ongoing

Operate

Cost-per-task, success rate, monthly tuning.

Technologies we use

The stack we ship in production.

Layer · 01

On-device

Phi-3
Gemma
Llama-edge
mlx
Layer · 02

Cloud LLM

ClaudeClaude
GPT-4
BedrockBedrock
Vertex
Layer · 03

Voice/Vision

Whisper
Core ML
MediaPipe
Industries we serve

Verticals where we've shipped, not where we dabble.

V·01

Healthcare

Patient triage, clinical notes

Signal<300ms On-device latency
V·02

Legal

Document review, drafting

SignalLocal Privacy
V·03

Field services

Diagnostics copilot

Signal200+ Eval cases
V·04

Education

Tutoring, grading

Signal−41% Cost-per-task
V·05

Sales

Call summarisation, drafts

Signal<300ms On-device latency
V·06

Productivity

Smart notes, scheduling

SignalLocal Privacy
Why Abstriq

Built for teams who ship fast in production.

We're not a body shop or a freelance marketplace. We run a senior-heavy engineering org with clear practice leads, real code review, and real on-call coverage.

Signal
<300ms
On-device latency
Signal
Local
Privacy
Signal
200+
Eval cases
Signal
−41%
Cost-per-task
Pillar · 01

Vertical AI, not just LLMs

We build agents that actually move numbers — wired into your systems, your data, your workflows.

Pillar · 02

Ship in weeks, not quarters

Small senior squads, tight feedback loops, code-merge every day, demo every week.

Pillar · 03

Industrial-grade rigor

From PLCs to SOC2 — production safety, observability, and resilience baked in.

Pillar · 04

One team across four worlds

Web, mobile, AI, and industrial under one roof — no vendor ping-pong.

FAQs

The questions we hear most.

If yours isn't here — ping us and we'll reply with specifics on your stack.

01On-device vs cloud LLM?
On-device for low-latency and privacy-sensitive paths. Cloud for higher-accuracy or larger context. We pick per use case in week one.
02Hallucinations in production?
Citations required above confidence threshold, eval harness in CI, eval-driven iteration. Kill switch on regressions.
03What about cost spike from cloud LLMs?
Cost-per-task dashboard, per-action budgets, monthly tuning, fallback to smaller models when applicable.
04Agent actions — risk surface?
Confirmations for real actions (book, buy, edit). Drafts-only for risky actions by default. Audit trail for every agent call.
Free consultation

Ready to ship ai / ml-based apps?

Tell us the problem — proposal in under 48 hours. NDA-friendly by default, senior-led, and we typically walk you through a proof-of-concept before the engagement closes.

Reply in 24hNDA-friendlySenior-ledProduction on-call

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