Four disciplines.One standard.
Built for growth-stage and enterprise teams whose operations depend on custom software, connected data, or regulated workflows. Across web, mobile, Salesforce, and applied AI, the goal is the same: less manual work, trusted information, and capacity the business can plan around.
WEB
Growth is outrunning the tools.
A platform that stays ahead of demand.
You have a workflow that is your competitive advantage and a patchwork of tools carrying it: spreadsheets, re-keyed data, and people quietly absorbing the load. Or a platform that got you here and cannot take the next step without a risk nobody can size. We replace the manual handoffs, integrate the systems you already run, and establish measurable capacity for the next stage of growth.
By the numbers
- The average enterprise runs 957 applications. 27% of them are integrated, and IT teams spend 36% of their time building custom integrations. Salesforce / MuleSoft, 2026 Connectivity Benchmark
What we build
Customer portals and self-service platforms
Internal operations and back-office systems
E-commerce and subscription platforms
API design and integration with the systems you already run
Modernization of platforms that cannot take the next step
Cloud infrastructure, defined as code
What changes
Capacity is a known number, not a guess
One record, entered once, correct everywhere
Staff stop absorbing the load by hand
Regression tests protect the workflows already running
Peak demand is tested against an agreed target
New volume and workflows extend the platform instead of the patchwork
- Next.js
- React
- Vue
- TypeScript
- Node
- Rust
- AWS
- Google Cloud
- Cloudflare
MOBILE
Your customers will judge the whole company by the app.
Apps people rely on every day, still shipping in year three.
The app is where your customers, or your field staff, meet the company, and they forgive nothing: a crash, a slow screen, a sync that silently fails. Most of the risk is not in the interface. It is in the backend behind it, and in whether anyone will still be maintaining it three years from now. We build both halves, choose native or cross-platform on evidence about your app rather than our preference, and stay on after launch.
By the numbers
- 46% of Android app installs are uninstalled within 30 days, and most of those uninstalls happen on day one. AppsFlyer, App Uninstall Report 2025
- Of the 7.77 million app submissions Apple reviewed in 2024, 1.93 million were rejected, most of them under the guideline that covers crashes, bugs, and incomplete apps. Apple, 2024 App Store Transparency Report
What we build
Native iOS (Swift) and Android (Kotlin)
Cross-platform (React Native, Flutter) when one codebase is the right call
Low-connectivity design for field use
The backend, APIs, and sync behind the app
App Store and Play Store release management
Analytics, crash reporting, and performance monitoring
What changes
Works in the field, on a bad connection, on the first try
Native or cross-platform chosen on evidence, not preference
The backend is engineered, not an afterthought
OS updates stop being a threat
Releases keep shipping in year three
You see how the app is actually used
- Swift
- Kotlin
- React Native
- Flutter
- App Store
- Analytics
SALESFORCE
The CRM nobody trusts.
A CRM that is finally the system of record.
You bought Salesforce to be the system of record, and it became the place where the numbers go to disagree: pipeline in the CRM, truth in a spreadsheet, integrations held together by exports, and an admin backlog no one can clear. The fix is rarely more licenses. It is engineering: custom Apex and Lightning where configuration ran out, integrations that keep every system agreeing, and data work that makes the reports believable. We have done this work for two decades, for Fortune Global 500 orgs with tens of millions of records, and from inside Salesforce itself.
By the numbers
- Data leaders estimate that 26% of their organization's data is untrustworthy and 19% is siloed or unusable. Salesforce, State of Data and Analytics 2025
What we build
Sales, Service, and Marketing Cloud implementation
Custom Apex and Lightning Web Components where configuration runs out
Integration with portals, ERPs, billing, and the rest of your stack
Data migration, cleansing, and deduplication
Org health, performance, and technical-debt remediation at scale
Ongoing administration and proactive support
What changes
One customer record, trusted by sales, service, and finance
Reports leadership stops second-guessing
Integrations that stop needing a human in the middle
Predictable performance as records and users grow
The admin backlog cleared, and kept clear
Data clean enough to put AI on top of
- Sales Cloud
- Service Cloud
- CPQ
- Field Service
- Apex
- LWC
- Integrations
- Data
AI / ML
The AI pilot demoed well. It never shipped.
AI that reaches production and stays there.
You have a mandate to use AI and a pilot that impressed everyone and shipped nowhere, because in production it has to be accurate on your data, safe with your customers', auditable for your regulators, and cheaper than the person it is helping. That gap is engineering, not prompting. We build LLM applications and agents with measured accuracy, a known cost per task, guardrails for costly errors, and the data pipelines required for dependable output. Where AI is not the answer, we say so.
By the numbers
- Only 25% of organizations have moved even 40% of their AI pilots into production, and only 21% have a mature model for governing AI agents. Deloitte, State of AI in the Enterprise 2026
- The leading root causes of failed AI projects: the wrong problem or metric, missing data, technology-first thinking, inadequate infrastructure to deploy, and problems too hard for AI. RAND Corporation (2024)
What we build
LLM applications and AI agents on your own data (RAG)
Workflow automation with a human in the loop where it matters
Evaluation suites, so accuracy is a measured number
Guardrails, audit logging, and HIPAA-grade compliance built in
Classification, extraction, and NLP for high-volume workflows
Predictive models, MLOps, and the data pipelines underneath
What changes
Accuracy is a number you can show the board
It runs on your data without your data leaving your control
A human stays in the loop where the cost of error is high
Cost per task is known before you scale it
It survives the model upgrade after next
An honest answer when AI is not the answer
- LLMs
- AI agents
- RAG
- Evaluation
- Guardrails
- NLP
- Predictive models
- MLOps
Your problem is specific.So is our answer.
Most of what we build does not fit neatly in one of the four boxes above: a portal that has to talk to Salesforce and an EHR, a mobile app whose real complexity is the backend, an AI workflow that only works because the data pipeline underneath it does. Tell us the problem, not the category. We will tell you what it takes, and if a smaller build or an off-the-shelf tool would get you there, we will say that too.
Tell us whatneeds to work better.
A workflow consuming staff time, a CRM nobody trusts, a platform slowing growth, or an AI initiative that needs a business case. Your free consultation is a working session: we identify the operating outcome, the biggest risk, and the right first milestone.
hello@amtal.dev- Response time
- Within one business day
- Engagements
- Open