AI Development Company in Los Angeles

Custom AI built for Los Angeles operators, not demos. We ship LLM applications, autonomous agents, computer vision and predictive models into the systems your team already runs, working Pacific hours from first call to handover.

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From Pilot to Production: Our Complete Suite of AI Development Services

Los Angeles runs on media libraries, freight, patient records and customer conversations, all of it data that people still process by hand. Six service lines cover the work end to end, from the first evaluated prototype to the model your team operates without us.

Engineer reviewing a large language model application interface

01 · Service line

LLM & Generative AI Applications

Retrieval-augmented generation over your own content
Drafting, summarising and Q&A inside existing tools
Guardrails, evaluation sets and hallucination testing
Prompt and cost optimisation per workload
GPT, Claude, Gemini, Llama and Mistral

Explore LLM Apps

Autonomous AI agent workflow visualised on a screen

02 · Service line

AI Agents & Workflow Automation

Agents that plan, call tools and finish real tasks
CRM, ERP and helpdesk workflow integration
Human-in-the-loop approval on anything consequential
Multi-step research, triage and reporting
Full audit trail of every action taken

Explore AI Agents

Predictive analytics dashboard showing model forecasts

03 · Service line

Machine Learning & Predictive Models

Demand, churn and revenue forecasting
Recommendation and ranking systems
Anomaly and fraud detection
Feature pipelines your team can rebuild
Honest baselines before any deep model

Explore Predictive ML

Computer vision model detecting objects in a video feed

04 · Service line

Computer Vision Systems

Media asset tagging and content moderation
Quality, safety and defect inspection
OCR and document understanding at volume
Edge deployment where latency matters
Retraining loops as your footage changes

Explore Computer Vision

Voice AI assistant handling a live customer call

05 · Service line

Conversational & Voice AI

Voice agents that book, qualify and follow up
Support assistants grounded in your knowledge base
Live call transcription and summarisation
Telephony and WhatsApp channel integration
Escalation to a human at the right moment

Explore Voice AI

MLOps pipeline monitoring dashboard on a workstation

06 · Service line

MLOps & Model Deployment

CI/CD for models, not just for code
Drift monitoring and scheduled retraining
Cost, latency and quality dashboards
AWS, Azure, GCP or your own tenancy
Runbooks so your team can operate it

Explore MLOps

Business Case

What Do Los Angeles Companies Get From Custom AI?

A demo that impresses in a meeting is the easy part. The return comes from what changes in the working day afterwards, so here is where custom AI actually pays for itself.

Hours back, every week

The work that quietly eats a team is reading, sorting and re-typing. Models take the first pass, and your people review the exceptions instead of processing everything.

Documents read, classified and routed
Drafts prepared, not written from scratch
People review, the model does the typing

Decisions made on evidence

Forecasting, scoring and anomaly detection turn the history you already hold into a number someone can act on this week, not a report nobody opens.

Demand and revenue forecasting
Churn and lead scoring that ranks honestly
Anomalies surfaced before they compound

Systems that survive contact

A pilot that works on one laptop and a system that runs on Monday morning are different builds. We design for the second one from the start.

Evaluation sets, so quality is measured
Drift monitoring and scheduled retraining
Cost and latency tracked per workload

A better experience to sell

Answers in seconds instead of a ticket queue, and personalisation that reflects what a customer actually did rather than the segment they landed in.

Support answered around the clock
Recommendations grounded in real behaviour
New AI-led tiers your competitors lack

Machine learning engineer reviewing model output on a workstation

AI That Earns Its Place in the Work

We do not ship a chatbot and call it transformation. Six engineering disciplines turn your own data into decisions, backed by our wider AI development and cloud teams. Want agents that take action rather than answer questions? See AI agent development.

[1] Retrieval-Augmented Generation

Answers grounded in your own documents and data, with citations, so the model cannot invent a policy that does not exist.

[2] Autonomous AI Agents

Agents that plan a task, call your tools and finish it, with a person approving anything that costs money or leaves the building.

[3] Predictive Modelling

Forecasting, scoring and ranking trained on your history, benchmarked against a simple baseline so the gain is provable.

[4] Computer Vision

Tagging, inspection and document understanding across images, video and scans, deployed at the edge when latency matters.

[5] Conversational & Voice AI

Assistants that hold a real conversation over chat or phone, grounded in your knowledge base and escalating cleanly to a person.

[6] MLOps & Evaluation

Versioning, drift monitoring, retraining and cost dashboards, so quality is a number you watch rather than a feeling.

Technologies

The AI Stack We Build On for Los Angeles Teams

The techniques we reach for when a model has to do more than sound convincing. Pick one to see where it earns its place in a build.

Large Language Models

GPT, Claude, Gemini, Llama and Mistral, chosen per workload rather than per press release. We benchmark cost, latency and accuracy on your own tasks, and will tell you when a smaller model does the same job for a tenth of the spend.

Retrieval & Vector Search

Your contracts, scripts, tickets and wikis made searchable by meaning rather than keyword, then fed to the model as grounded context with citations. Built on pgvector, Pinecone or OpenSearch depending on where your data already lives.

AI Governance & Guardrails

Input and output filtering, PII redaction, refusal handling and an audit log of every prompt and response. Under CCPA and CPRA this is not optional polish, it is how you answer a regulator without guessing.

Fine-Tuning & Distillation

When prompting stops improving, we fine-tune on your labelled examples or distil a large model into a smaller one you can run cheaply. Only after retrieval and prompting have been exhausted, because they usually win on cost.

Evaluation & Observability

A held-out test set, automated scoring and regression checks on every prompt or model change. Quality becomes a number on a dashboard instead of an argument about whether last week felt better.

Vision & Multimodal Models

Models that read images, video and scanned paper as fluently as text. In Los Angeles that most often means media libraries, claims documentation and inspection footage that nobody has time to tag by hand.

Private & Self-Hosted Deployment

Open-weight models running inside your own VPC or on-prem, so regulated data never leaves your tenancy. The right call for healthcare and finance, and increasingly for anyone with a strict DPA.

Large Language Models Retrieval & Vector Search AI Governance & Guardrails Fine-Tuning & Distillation Evaluation & Observability Vision & Multimodal Models Private & Self-Hosted Deployment

Talk to an AI Engineer

Weighing Up an AI Project? Get a Free Use-Case Audit.

One call about your data, your workflow and what you are trying to change tells you which use case is worth funding first, what it will realistically cost and where AI is the wrong tool. So your first dollar goes somewhere that pays back.

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Free · No obligation · Delivered within 48 hours

Consultant and client agreeing an AI project scope

Industries

AI Built for Los Angeles Industries

Six sectors that shape the LA economy, from studio back catalogues to freight moving through the ports. See our healthcare and real estate practices for deeper detail, or enterprise software when the AI has to reach the rest of the business.

[01] Media & Entertainment

Back catalogues that finally become searchable, and the tagging nobody has time to do.

Automatic scene and asset tagging
Script and treatment analysis
Localisation and subtitle drafting

Know more about Entertainment

[02] Healthcare & Life Sciences

Clinical paperwork handled under HIPAA, so clinicians spend the hour with the patient.

Clinical note summarisation
Prior authorisation triage
Patient intake and follow-up agents

Know more about Healthcare

[03] Logistics & Freight

Port and warehouse operations where a document backlog is the real bottleneck.

Customs and BOL document extraction
ETA and demand forecasting
Exception handling and routing agents

Know more about Travel

[04] Hospitality & Travel

Listings, leases and comparables read at portfolio scale rather than one PDF at a time.

Lease abstraction and clause review
Automated valuation support
Tenant enquiry assistants

Know more about Logistics

[05] FinTech & Financial Services

Underwriting, fraud and compliance work where an audit trail is not negotiable.

Fraud and anomaly detection
KYC and document verification
Risk scoring with explainability

Know more about Insurance

[06] Retail & E-Commerce

DTC brands where merchandising and support scale faster than headcount can.

Product copy and enrichment at scale
Recommendations from real behaviour
Support assistants that resolve, not deflect

Know more about Retail

[07] Education & EdTech

Course material, marking and student support handled without diluting teaching quality.

Adaptive content generation
Assisted marking with rubrics
Always-on student assistants

Know more about Education

[08] Insurance & Claims

Claims files, photographs and policy wording read together instead of in three queues.

First-notice-of-loss triage
Damage assessment from photos
Policy wording comparison

Know more about FinTech

[09] Real Estate & PropTech

Booking, review and guest-service work across properties and time zones.

Multilingual guest assistants
Review analysis and response drafting
Dynamic rate recommendations

Know more about Real Estate

Measurable Impact

Numbers From Shipped AI Builds

200+

Products shipped

Delivered since 2018 across AI, mobile, cloud and enterprise builds.

80%

Calls handled by AI

Share of inbound calls resolved end to end by the AI voice agent we built for a healthcare client.

60%

Fewer missed calls

Drop in missed patient and enquiry calls after the voice agent went live.

15+

Countries served

Client operations running on Appther-built platforms worldwide.

Case Studies

Work We've Shipped

AI builds we can point at: a 24/7 voice agent handling patient calls, a WhatsApp assistant carrying frontline support, and a routing engine trained on live logistics data.

AI voice agent app showing live call transcription and booking and Bluetooth weigh scales

LIVE IN PRODUCTION

Voice AIBLE Scales

Waste Collection Monitoring Platform

A 24/7 AI receptionist that books appointments, answers patient questions and follows up, deployed on a dedicated server.

38,412Bins Scanned / Day

312 tWeighed Daily

96%Scan Compliance

View Case Study

WhatsApp AI assistant answering a customer conversation

LIVE IN PRODUCTION

LLMWhatsApp

WhatsApp AI Customer Assistant

An assistant grounded in the client’s own knowledge base, answering customer questions on WhatsApp and handing over to a person the moment it is unsure.

95%Fewer Disputes

70%Faster Payouts

2,400+Farmers Onboarded

View Case Study

Logistics fleet dispatch optimised by an AI routing engine

LIVE IN PRODUCTION

TelematicsRoute AI

AI/ML Logistics Optimisation

An AI routing engine trained on live telematics from a 120-vehicle fleet, cutting empty miles across daily dispatch.

28%Lower Fuel Cost

35%Faster Deliveries

94%Satisfaction

View Case Study

Gain a Competitive Edge

Why Los Angeles Teams Pick Appther for Custom AI Development?

Plenty of agencies can demo a chatbot. Fewer will run your own data through an evaluation set in week one and tell you plainly what a model can and cannot do for you. As a custom AI development company we cover the whole connected stack in house, alongside our AI development and cloud infrastructure teams.

One team, whole stack

Data pipeline, retrieval, model and interface under one roadmap, through to the APIs your other systems consume. No three-vendor standoff when an answer goes wrong somewhere between the data and the interface.

Measured, not demoed

Every model ships with a held-out test set and a baseline to beat. If the AI does not outperform the simple approach, we say so rather than shipping it anyway.

Built into the work, not beside it

As an AI-first company we put the model where the work is: inside the CRM, the helpdesk and the back office, rather than behind a separate login. Where itn the link drops. Where it earns its keep we build digital twins on the live stream, backed by our AI engineering practice.

Fixed scope, fixed price

Discovery ends with a written scope and a fixed quote. You own 100% of the code, prompts, fine-tuned weights and documentation, with an NDA signed up front.

Security & Compliance

How Does Appther Secure Your AI Data?

Training and prompting put your data somewhere new, which carries real obligations. Here is how we handle each category, and only what we can honestly claim.

Prompts and training sets contain personal data more often than teams expect. We design around minimisation first, so compliance is a property of the architecture rather than a later retrofit.

GDPRLawful basis mapped per data type, EU residency options, and working export and deletion endpoints built in from the first sprint.

CCPA / CPRACalifornia disclosure, opt-out and deletion workflows wired through the prompt log, the vector store and the training set, not just the CRM.

India DPDP ActConsent capture and notice flows for deployments serving Indian users, with purpose limitation on training data.

Data minimisationWe send the model the fields the task needs and redact the rest before it leaves your systems, which cuts both risk and token cost.

A model is only as safe as the weakest path into it. Identity, transport and prompt handling are treated as one system, not three separate features.

Tenant isolationPer-tenant keys and namespaces so one customer’s documents can never surface in another customer’s retrieval results.

Encrypted transportTLS on every model call, AES-256 at rest for embeddings and prompt logs, and private networking to the inference endpoint.

Prompt-injection defenceUntrusted content treated as data, never as instructions, with tool access allow-listed and output filtered before it reaches a user.

Least-privilege toolsAgents get the narrowest scope that does the job, and anything that spends money or leaves the building needs a human approval.

The controls below describe how we build and review code. They are engineering practices we apply on every project, not certifications we hold.

OWASP Top 10 for LLMsReviewed against every release, covering prompt injection, insecure output handling and excessive agency.

ISO/IEC 27001 alignedAccess control, change management and audit logging modelled on the standard's technical controls.

Secure coding & reviewPeer review on every merge, dependency scanning in CI, and secrets management outside the repository.

Penetration testingThird-party testing arranged before launch on request, with findings tracked to closure.

To be explicit: Appther is not a certification body. Formal ISO, SOC 2 or HIPAA attestation of your finished product sits with your compliance team and its auditor. We build and document so that audit is straightforward.

Regulated sectors change the architecture, not just the paperwork. We scope these constraints during discovery so they never surface late.

HIPAA-ready buildsFor clinical workloads we architect to the technical safeguards: access control, audit logs and BAA-compatible model endpoints.

PCI DSS scope reductionCard data redacted before any prompt leaves your environment, so the model never widens your compliance scope.

Model transparencyDecisions that affect a person come with the retrieved evidence attached, so a reviewer can see why the model said what it said.

Data residencyUS-only or in-tenancy inference where contracts demand it, flagged in discovery rather than discovered at security review.

AI features fail review and user trust for predictable reasons. We build against the rules and expectations that trip up AI products specifically.

Disclosure of AI usePeople are told when they are talking to a model and how to reach a person, which is both good practice and increasingly required.

Human escalation pathsEvery assistant has a clean handover to a person, with the conversation context carried across so nobody repeats themselves.

WCAG 2.2 AAContrast, focus order and screen-reader labelling checked on the AI interface, not just the marketing site.

Failure statesWhen the model is unsure it says so and offers a route forward, instead of inventing an answer with full confidence.

Tech Stack

Models, Platforms & Frameworks

The AI development stack we build on, from model selection and retrieval through to vector and time-series data and performance work once the model is live.

From the model that answers to the pipeline that keeps it honest, grouped by the layer it belongs to.

Models & Providers Frameworks & Orchestration Data & Vector Stores Cloud, MLOps & Delivery

OpenAI
Anthropic Claude
Google Gemini
Meta Llama
Mistral
DeepSeek
Qwen
Hugging Face

LangChain
LangGraph
CrewAI
PyTorch
TensorFlow
FastAPI
Python
Node.js

pgvector
Milvus
Qdrant
Redis
MongoDB
TimescaleDB
InfluxDB
Grafana

AWS Bedrock
Azure OpenAI
Google Vertex AI
Docker
Kubernetes
Terraform
React
Twilio

Feature Set

Key Features We Deliver in AI Applications

The building blocks that show up in most AI platforms we ship, from the first retrieval query through to production monitoring.

Document Q&A Over Your Own Data
Autonomous Task Agents
Scheduled Model Retraining
Streaming Responses & Live Transcription
Forecasting & Churn Prediction
Model Quality & Cost Dashboards
End-to-End Encryption
Per-Tenant Isolation & Access Control
Multi-Model Routing (GPT, Claude, Gemini, Llama)
ERP, CRM & Helpdesk Integrations
Role-Based Access Control
Alerts & Human Escalation
Prompt & Model Versioning
Computer Vision & OCR
Evaluation & Drift Analytics
Token & Inference Cost Tuning

Show all 16 features Show fewer

Architecture

AI Architecture, Layer by Layer

A production AI system is ten layers that have to agree with each other. Get one wrong and the symptom shows up somewhere else, usually as an answer nobody trusts, which is why we design them together rather than in sequence.

Data Sources

The systems the answers have to come from: your CRM, document store, data warehouse, ticket history and whatever still lives in a shared drive.

Ingestion & Cleaning

Parsing, de-duplication and chunking. Most disappointing AI projects are a data problem wearing a model problem’s clothes.

Embeddings

Text, images and audio turned into vectors so meaning, not keywords, decides what the model is shown.

Vector Store

pgvector, Qdrant or Milvus, sized for your corpus and partitioned per tenant so retrieval can never cross a customer boundary.

Retrieval

Hybrid search and re-ranking that puts the right passages in front of the model, with citations carried through to the answer.

Model Layer

The model itself, hosted or self-run, chosen per task against cost, latency and accuracy rather than reputation.

Orchestration

Prompts, tools, memory and multi-step plans. Where an agent decides what to do next and which system it is allowed to touch.

Guardrails

Input and output filtering, PII redaction and refusal handling, plus the audit log that lets you answer a CCPA request honestly.

APIs & Integration

The answer delivered into Salesforce, the helpdesk or your own product, because a model behind a separate login gets used twice.

Evaluation & Monitoring

Held-out test sets, drift detection and cost dashboards, so quality is a number you watch rather than a feeling you have.

Data Sources

The systems the answers have to come from: your CRM, document store, data warehouse, ticket history and whatever still lives in a shared drive.

Ingestion & Cleaning

Parsing, de-duplication and chunking. Most disappointing AI projects are a data problem wearing a model problem’s clothes.

Embeddings

Text, images and audio turned into vectors so meaning, not keywords, decides what the model is shown.

Vector Store

pgvector, Qdrant or Milvus, sized for your corpus and partitioned per tenant so retrieval can never cross a customer boundary.

Retrieval

Hybrid search and re-ranking that puts the right passages in front of the model, with citations carried through to the answer.

Model Layer

The model itself, hosted or self-run, chosen per task against cost, latency and accuracy rather than reputation.

Orchestration

Prompts, tools, memory and multi-step plans. Where an agent decides what to do next and which system it is allowed to touch.

Guardrails

Input and output filtering, PII redaction and refusal handling, plus the audit log that lets you answer a CCPA request honestly.

APIs & Integration

The answer delivered into Salesforce, the helpdesk or your own product, because a model behind a separate login gets used twice.

Evaluation & Monitoring

Held-out test sets, drift detection and cost dashboards, so quality is a number you watch rather than a feeling you have.

Techniques we build on

RAGFine-tuningDistillationFunction callingMulti-agentGuardrailsHybrid searchRe-rankingEvals

Which one fits is decided by your data, latency budget and accuracy bar, not by what is trending. See our AI agent development and AI development services for the layers either side of the model.

Our Process

End-to-End AI Development Process

Thirteen stages take an AI idea from first conversation to a system your team runs without us. Model work punishes guesswork, so the risky parts get proven against your own data early and you see a measured result at the end of every stage.

Week 1

Discovery

Use-case analysis, constraints and success criteria before any model or platform is chosen.

What we doSit with the people doing the work today, find where the hours actually go, and check whether your data can support the use case at all. Existing tools and contracts get reviewed here, not later.

What you getA written problem statement, the metric that decides success, and a shortlist of use cases ranked by return rather than novelty.

Week 1–2

Business Case

What the AI has to be worth before it is worth building.

What we doModel the hours or revenue the deployment is meant to produce, size the token and infrastructure cost at launch and at scale, and agree the number we will be judged on.

What you getA business case with the arithmetic behind it, and a scope you can hold us to.

Week 2–3

Data Readiness

An honest look at whether your data can carry the use case.

What we doAudit coverage, labelling quality and access. Build the ground-truth set now, because without one there is no honest way to say later whether the model improved.

What you getA data assessment, a cleaned evaluation set, and a plain answer on anything that needs fixing first.

Week 3–5

Prototype & Evaluation

A working prototype measured against a baseline, not a demo video.

What we doBuild the simplest thing that could work, score it against the held-out set, then try the more expensive approach and see whether it actually wins.

What you getA prototype running on your data, a scorecard against the baseline, and a recommendation you can act on.

Week 4–7

Model Selection

Choosing the model against cost, latency and accuracy on your own tasks.

What we doBenchmark candidate models head to head, including the small cheap ones. Decide hosted or self-run based on your data constraints rather than preference.

What you getA documented model choice with the benchmark behind it, and the cost per thousand requests you should expect.

Week 5–10

Retrieval & Data Pipeline

The plumbing that decides what the model gets to see.

What we doChunking, embedding, hybrid search and re-ranking, with per-tenant partitioning so retrieval cannot cross a customer boundary.

What you getA retrieval layer with measurable recall, and pipelines your own team can rebuild without us.

Week 7–13

Orchestration & Agents

Prompts, tools and multi-step plans wired to real systems.

What we doDefine which tools the agent may call, what needs a human approval, and how it behaves when a step fails or the answer is not in the data.

What you getWorking agent flows with an audit trail of every action taken.

Week 8–14

Guardrails & Compliance

The safety layer, designed in rather than bolted on.

What we doInput and output filtering, PII redaction, refusal handling and prompt-injection defence, mapped to CCPA and CPRA obligations and your own DPA.

What you getA documented guardrail layer and an audit log that answers a regulator without guesswork.

Week 9–16

Integration

Getting the output into the tool your team already uses.

What we doConnect to Salesforce, HubSpot, NetSuite, SAP, Odoo, Snowflake or your own product through secure, versioned APIs.

What you getAI in the daily workflow rather than behind a separate login nobody opens twice.

Week 10–17

Interface & UX

The part your people actually touch.

What we doDesign for the moment the model is unsure: show the evidence, offer a route forward, and make escalation to a person one click rather than a dead end.

What you getAn interface people trust enough to keep using in week six.

Week 14–20

Evaluation & Hardening

Proving it holds up before anyone depends on it.

What we doRegression testing on every prompt and model change, adversarial testing, load testing, and cost profiling at production volume.

What you getA quality baseline you can regress against, and known behaviour under load.

Week 18–22

Deployment

Production rollout, staged so a bad change never reaches everyone.

What we doCanary release behind a feature flag, monitoring on quality, latency and spend from day one, and a documented rollback.

What you getA live system, runbooks, and a team trained to operate it.

Ongoing

Monitoring & Retraining

The years after launch, which is where most of an AI system’s life happens.

What we doWatch for drift as your data and language change, retrain on a schedule, and re-benchmark when a materially better model ships.

What you getQuality that holds up over time instead of quietly decaying, with a monthly report you can read.

Client Voices

Trusted By Teams Who Ship

**Bob Singhal**Professor of Joy · Joyscore Inc

**Johnathan Peffer**CEO · Bull's Eye Technologies Inc

**Pushpender Kumar**General Manager · AB Sugar Ltd

Latest Insights

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Field notes from real builds. No hype, just what worked and what didn't.

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A phase-by-phase breakdown of what a connected build actually costs.

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How the companion app fits the wider system, and the stack we reach for.

Agentic AI and the Autonomous Workforce

What changes when AI moves from a demo into the daily workflow.

Benefits of Smart Waste Management Systems

An AI deployment running in production, and the numbers behind it.

Free 30-Minute AI Strategy Call

Connected Products Start With One Conversation. Yours Starts Here.

Walk away from one call with a prioritised use case, a realistic budget range and a delivery timeline. No pitch deck, no obligation.

Protocol shortlist Budget range Delivery timeline

Book My Free Call

200+Products shipped

4.9★Clutch client rating

15+Countries served

99.9%Production uptime

FAQ

Questions We Hear Every Week

It depends on scope. A focused pilot, one use case with a working model and a real integration, usually starts in the low five figures. Production platforms with several models, MLOps and enterprise integrations run higher. You get a fixed-price quote after a free scoping call, with no open-ended billing.

A proof of concept running against your own data typically takes 4 to 6 weeks. A production system with monitoring, retraining and integrations usually lands in 3 to 6 months, depending on how ready your data is. Data readiness moves that timeline more than model choice does.

Yes. Our US-facing team covers Pacific hours, so stand-ups, demos and escalations happen inside your working day rather than overnight. Delivery runs as a distributed team with a named senior engineer as your point of contact, not a rotating support queue.

GPT, Claude, Gemini, Llama and Mistral, deployed on AWS Bedrock, Azure OpenAI, Google Vertex AI or self-hosted, using LangChain, LlamaIndex and Hugging Face. We pick per use case and tell you plainly when a cheaper or smaller model would do the same job.

California's CCPA and CPRA shape how a model may use personal data, so we design for it from the start: data minimisation, documented retention, deletion paths that actually reach the training set, and the option to keep everything inside your own cloud tenancy. We sign an NDA before discovery and can work under your DPA.

Yes, and that is usually where the value is. We connect models to Salesforce, HubSpot, NetSuite, SAP, Odoo, Snowflake and in-house databases through secure APIs, so the output lands in the tool your team already uses instead of another dashboard nobody opens.

It depends on your data sensitivity, volume and cost profile. Commercial APIs get you to production fastest and suit variable workloads. Self-hosted open models make sense at high, steady volume or where data cannot leave your tenancy. We model both costs during discovery and show you the crossover point.

Yes. You own 100% of the code, prompts, fine-tuned weights and documentation we produce, along with the training pipelines. Nothing is locked to a platform only we can operate, and we hand over runbooks so your team can take it on.

That is what we recommend. A fixed-price pilot proves the use case against your real data and gives you a measured result before anyone signs off on a platform build. If the numbers do not hold up, we will tell you, and you have not spent a production budget finding out.

Media and entertainment, logistics and freight around the ports, healthcare, fintech, real estate, aerospace suppliers and direct-to-consumer commerce. The common thread is not the sector but the shape of the problem: large volumes of messy documents, media or operational data that people currently process by hand.

An Engineer Replies Within One Business Day

Let's Build Your Connected Product

Tell us what you want to sense, control or monitor. No sales pitch, just a straight answer on approach, scope, timeline and cost. Prefer email? Use our contact page.

Free 30-min strategy callScoping and honest, practical advice.

Fixed-price quoteDetailed scope and price after discovery, no surprises.

NDA on requestYour idea, designs and code always stay yours.

Prefer to talk now?+91 99114 32288 · sales@appther.com

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