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AI Development Services

Custom AI Built for Production From Day One

Tech.us is a San Jose based AI development company. We select the right architecture for the problem, then build and deploy it inside the environment your data requirements allow.
Our guardrails and governance frameworks are our own, so the safety layer can be adapted to your requirements rather than waiting on an upstream provider.

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Projects Delivered

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Years of Engineering expertise

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Industries Served

Trusted by organizations that depend on technology

Where AI programs stall

Adoption Is No Longer the Problem

AI adoption is widespread, but scaling it across the business is still difficult. McKinsey found that while 88% of organizations use AI in at least one function, only 7% have fully scaled it.

  • Connect AI to real business data and systems
  • Build accountability into production workflows
  • Protect sensitive information while keeping work accessible
  • Keep AI dependable as workflows become more complex
Source: McKinsey, AI at work but not at scale

OUR SERVICES

What Production AI Systems Depend On

We build AI systems that help businesses work more efficiently and make better use of their data. They are built to perform reliably in day-to-day business operations.

Agentic AI and Workflow Automation

We build autonomous agents that reason through tasks, use business tools, and act across connected systems, escalating decisions that need human approval.

Multi-Agent Systems and Orchestration

For complex workflows we decompose the process into four roles: research, extraction, validation, and reporting. Each runs as a specialized agent under an orchestration layer, validating critical outputs before the process moves forward.

Enterprise RAG and Knowledge Assistants

Our enterprise RAG pipeline runs six stages: ingestion, intelligent chunking, embeddings, vector search, retrieval, and citation. Chunking is where retrieval quality is usually won or lost, which is why it is a stage rather than a step inside one.

AI Guardrails and LLM Safety Framework

Our guardrails framework is our own. It protects applications against prompt injection, jailbreaks, and data leakage, with input and output controls, PII protection, and policy enforcement configured to the boundaries you and your regulators require.

AI Governance and Responsible AI Framework

Audit trails, access controls, model versioning, and usage monitoring, built in from the start rather than added after an audit finding. Human review can sit in critical workflows so accountability holds as adoption scales.

Selected Work

Systems We Have Taken Into Production

See how Tech.us engineers systems around real business workflows, data, users, and operating constraints.

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AI-Powered Takeoffs From Complex Construction Drawings

A precast concrete manufacturer partnered with Tech.us to bring AI into the estimating workflow. The system uses OCR, computer vision, and generative AI to identify, extract, and visualize structures, pipes, and components from complex construction drawings.

Read the Case Study
Wellington Hamrick Precast AI takeoff automation tool shown on laptop and tablet

Mobile Fleet Visibility Built for Field Operations

SkyHawk by TELUS needed a streamlined mobile experience for its Connect Anywhere platform. Tech.us built the core experience around real-time asset tracking, fleet activity, secure configuration, and map-based operational visibility.

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SkyHawk by TELUS mobile app screens showing fleet tracking and login

Personalized Financial Technology Built to Scale

Tech.us helped bring Wealth Mastery to life as a digital platform that puts personalized financial planning tools directly in users' hands while supporting a large and growing audience.

Read the Case Study
Tony Robbins Wealth Mastery app shown on tablet and phone

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Why Tech.us

Why Businesses Trust Tech.us for AI Development

Many AI projects struggle because of the engineering around the model rather than the model itself. That is the work we are built for.

We Define the Right System Before Development Starts

A large share of AI failure happens when a technically capable system solves a problem adjacent to the one the business has. We separate the capabilities that get confused with one another before development begins.

We Control the Safety Layer

Our guardrails and governance frameworks are built by us rather than held purely as third-party dependencies. If your environment requires a change, we adapt the layer directly instead of waiting on an upstream provider.

Your Data Can Stay Inside Your Environment

On-premise and private-cloud deployment lets organizations use AI without automatically sending regulated information outside their infrastructure. We address that through architecture rather than through policy about how an external service should behave.

We Build Both Custom and Adapted Models

Where a leading proprietary or open-source model already fits, we adapt it. Where domain accuracy, privacy, or operating cost demands it, we train or fine-tune our own. The choice follows the use case.

AI-Assisted Code Is Reviewed by an Engineer

Code written with AI assistance passes human review before it reaches a client system. Faster authorship does not move accountability for what ships.

We Know What Happens After the Pilot

Moving a proof of concept into production, integrating it with real workflows, and keeping it reliable is a different discipline from building it. Across more than 1,500 projects and 26 years, production delivery is the stage we have handled repeatedly.

why-choose-techus

HOW WE WORK

Architecture First, Then a Narrow Build

1

Discovery and Architecture Selection

We establish the decision, the data, the systems, the error tolerance, and the deployment constraint, then recommend which architecture the requirement actually calls for. 

2

Feasibility on Your Own Material

We test the approach against a sample of your real data rather than a representative example, because that is where most surprises surface.

3

Proof of Concept With Acceptance Criteria

We build the contained version against criteria agreed in advance, so the result is a measured outcome rather than a demonstration. 

4

Production Engineering

We build the integration, permissions, guardrails, governance, monitoring, and exception handling that the contained version did not need. 

5

Deployment Into the Workflow

We put the output where the work already happens and define what people are expected to do with it, including where review remains necessary. 

6

Operation and Extension

We monitor performance, cost, and usage, adjust as data and requirements move, and apply the same foundations to the next use case. 

ENGAGEMENT

Flexible Engagement Models for AI Development

The right engagement depends on the scope of the problem and the complexity of the system required. Projects can begin with a smaller validation effort and expand once the result holds.

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Discovery and Feasibility Assessment

A structured assessment to identify use cases with meaningful business value, determine technical viability, and define a practical roadmap before development starts.

Proof of Concept to Production

A POC or MVP tests the core idea before a full implementation is committed. Once the approach proves itself, we expand what works into a production system, which shortens the path to measurable results and reduces the risk of investing in an unvalidated direction.

Dedicated AI Team

AI engineers and data specialists operating as an extension of your internal team, for organizations with ongoing requirements that should not each begin as a separate standalone project.

BEFORE WE QUOTE

Five Questions We Answer Before Recommending an AI Architecture

An AI project is priced and scoped on these five answers. Where any of them is missing, that is the finding, and we say so rather than proposing a build around it.

The decision that changes

Which specific decision or process improves, who owns it, and what they would do differently once the system exists.

The state of the data

Whether the information required exists in usable form, how it is spread across systems, and what preparing it will actually take. This is where most timelines are decided.

The interface into your systems

What the AI has to read and change, whether those systems expose usable interfaces, and what identity and permission model applies.

The tolerance for error

What an incorrect output costs in this workflow, which determines how much validation, human review, and control the system needs before it can run.

Where the data is permitted to go

Whether the workload can use an external model provider or has to remain inside your environment, since this shapes the architecture rather than the deployment step.

PRIVATE AND ON-PREMISE AI

Keep Sensitive Data Inside the Environment You Control

Where accuracy, data privacy, and inference cost all matter at once, routing every task to a general-purpose LLM API is not always the right architecture.

Domain-Specific Models for Specialized Work

We develop and fine-tune small language models around the terminology, data patterns, and workflows of a specific industry.

  • Tuned to domain-specific terminology and workflows
  • Built for work such as clinical documentation, contract analysis, and compliance review
  • Smaller models can reduce inference infrastructure requirements

Private Deployment for Data-Sensitive Workloads

Small language models can be deployed on-premise or within a private cloud so sensitive information remains inside the environment your organization controls.

  • On-premise and private-cloud deployment for data-sensitive workloads
  • Sensitive healthcare, legal, and financial data stays within your environment
  • Reduces reliance on external model providers for regulated work

Guardrails and Governance Around the Model

Private deployment controls where information resides. Guardrails control model behavior at runtime, and governance provides visibility into how AI is accessed and used over time.

  • Runtime protection against prompt injection, jailbreaks, and data leakage
  • Audit trails, access controls, and model versioning for traceability
  • Human-in-the-loop review for workflows requiring additional oversight

What we build

Applied AI Solutions

Systems shaped by production requirements rather than a generic demo retrofitted to a business problem.

Document Intelligence

We convert unstructured documents into structured data at scale, covering classification, extraction, OCR to JSON, table and signature recognition, redaction, comparison, deduplication, and splitting. Built for invoices, contracts, forms, receipts, and medical records across banking, insurance, legal, and healthcare.

Medical Imaging and Healthcare AI

We develop medical imaging AI across oncology, radiology, pathology, and cardiology, spanning cancer screening, tumor and lesion detection, bone and organ analysis, and cardiac assessment. These are decision-support systems that strengthen clinical judgment rather than replace it.

Computer Vision

We build systems that detect objects, identify defects, and monitor safety across images and video at volumes human review cannot keep pace with.

Visual Search and Image Enhancement

We build semantic image search, automated tagging, similar-image and region search, and enhancement including low-light restoration, for large catalogs in retail, e-commerce, design, and media.

Speech and Audio Intelligence

We build voice AI that handles customer calls end to end, combining telephony with speech-to-text, text-to-speech, and language models. Agents respond in context, call your APIs, and update CRM records mid-call, with transcription, speaker identification, and sentiment analysis running alongside.

NLP and Data Mining

We build NLP systems that classify sentiment and intent across support tickets, reviews, and correspondence, turning anecdotal evidence into something measurable.

Data Modernization and AI Reporting

Most AI projects stall on data rather than models. If one customer exists in three systems under two IDs, no model can answer reliably. We fix that foundation, then build reporting that takes plain-language questions instead of a dashboard queue.

Industries

Industries We Serve

The AI opportunity in healthcare looks very different from the one in manufacturing or financial services. We build around the problems that matter within each industry rather than applying the same use case everywhere.

Explore All Industries
Healthcare-2 Healthcare We build medical imaging AI for oncology and radiology, with additional applications in pathology and cardiology, alongside AI-assisted reporting and patient intake copilots. These are decision-support tools that reduce administrative effort while keeping clinical judgment with healthcare professionals. Explore Healthcare Finance Financial Services and Insurance We develop document intelligence for intake and claims workflows, and models that evaluate risk while transactions are still active. Private deployment allows regulated information to remain inside the organization's own environment where required. Explore Financial Services and Insurance Manufacturing-2 Manufacturing We use computer vision to identify product defects and monitor workplace safety conditions. Predictive models analyze equipment telemetry so teams can respond to developing problems before a failure becomes production downtime. Explore Manufacturing ind-retail-1 Retail and E-Commerce We build visual search and automated tagging for large product catalogs, with image enhancement supporting visual asset quality. Demand forecasting models convert sales history into forecasts teams can use for inventory and ordering decisions. Explore Retail and E-Commerce Preconstruction-2 Construction We develop systems that interpret drawings and site imagery and automate information-intensive workflows, including automated takeoff, where work that once required days of manual effort can be compressed substantially. Explore Construction ind-transportation-1 Transportation & Logistics We build document intelligence for shipping paperwork and customs documentation, and predictive models over fleet and telematics data that support routing, maintenance, and exception handling. Explore Transportation & Logistics

SCOPING

Three AI Distinctions That Matter Before You Build

These terms get used interchangeably even though the underlying systems solve different problems. We draw the lines before development starts, because the architecture decision is the one that is most expensive to reverse.

01

Agentic AI or Multi-Agent Systems

Agentic AI is a single autonomous agent that reasons through a task, works with tools, and carries a process from start to finish.

Multi-agent systems are coordinated groups of specialized agents operating under an orchestrator, validating one another's work.

How to tell: If one incorrect step can compromise the entire result, a system where agents independently verify important outputs is usually the better fit.

02

Enterprise RAG or Document Intelligence

Enterprise RAG lets employees ask questions and receive answers grounded in your private information, with sources provided for traceability.

Document intelligence takes documents in and converts them into structured fields, tables, or machine-readable JSON.

How to tell: If the result has to move into another application rather than be read by a person, extraction is the right architecture rather than a conversational interface.

03

AI Guardrails or AI Governance

Guardrails are runtime controls that protect an application against prompt injection and sensitive data leakage while reducing unsafe model behavior.

Governance is program-level control covering auditability, access management, version tracking, and ongoing monitoring.

How to tell: Guardrails determine what a model may do during an interaction. Governance gives the organization visibility into how AI has been operating over months.

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RECOGNITION

Awards and Recognitions

Tech.us has been recognized by leading industry platforms for its work across artificial intelligence and custom software development.

Mobile App Daily — Top Web Development Company
DesignRush — Top AI Development Company
Mobile App Daily — Top AI Company
TechReviewer — Top Mobile App Developers
TechReviewer Award

TECH STACK

The Stack Follows the Architecture

We work across commercial and open-weight models, agent frameworks and orchestration, retrieval and vector search, document and vision models, speech recognition and synthesis, fine-tuning and serving infrastructure, and the guardrail and observability tooling that keeps a system measurable in production.
Selection follows the architecture the use case calls for, accuracy on your own material, cost at your volume, where data is permitted to be processed, and what your team can maintain. Where a production-grade tool falls short, we extend it with our own frameworks.

Agentic AI & Orchestration

Agentic AI Agentic AI
AI Agents AI Agents
Multi-Agent Systems Multi-Agent Systems
Agentic Workflow Automation Agentic Workflow Automation
Model Context Protocol (MCP) Model Context Protocol (MCP)
Agent-to-Agent Protocol (A2A) Agent-to-Agent Protocol (A2A)
Agent Memory and Reasoning Agent Memory and Reasoning

FAQ

Questions Worth Asking

End-to-end AI development covering agentic AI, enterprise RAG, custom small language models, document intelligence, computer vision, medical imaging, speech intelligence, and generative AI. We cover the full lifecycle from use-case discovery through production deployment, monitoring, and continued optimization. 

Guardrails act while the model is running, preventing unsafe responses, data leakage, and manipulation of the model. Governance operates at the program level through audit trails, access controls, monitoring, and model versioning. Regulated organizations typically need both, because they address different risks.

Document intelligence turns an invoice or contract into fields, tables, or machine-readable JSON that another system can use. A RAG-based knowledge assistant lets people ask questions and get conversational answers. One produces structured data, the other produces answers for a person. 

A single agent works when the process is contained enough for one agent to complete reliably. Multi-agent systems suit work that divides into specialized responsibilities where agents can check one another's critical outputs, which is more reliable than one model managing everything. 

RAG connects a model to a live knowledge source, so responses stay current and traceable without retraining. Fine-tuning changes the model itself to specialize its behavior. Many systems use both, and the choice depends on how often your information changes, the accuracy required, and budget. 

Compact models designed or tuned for a focused domain or task. On specialized work they can outperform general-purpose LLMs on far less infrastructure, and they can be deployed on-premise or in a private cloud, which keeps sensitive information under your control. 

Yes. We design for on-premise and private-cloud deployment where sensitive information has to stay inside your environment, which is common in healthcare, legal, and financial work. Private deployment can be paired with our guardrails and governance frameworks. 

We ground models in verified information through RAG, validate responses before they are accepted, and use citation checks for traceability to the source. Our guardrails framework filters unsafe and non-compliant output, and a governance layer adds monitoring, auditability, and human review where a workflow needs it. 

Both. We adapt leading proprietary and open-source models where they already fit, and train or fine-tune our own when the domain demands more specialization. A fully custom approach is warranted when accuracy, privacy, or operating cost makes it the stronger requirement. 

We begin with a discovery and feasibility assessment to identify use cases with real business value and confirm the approach is viable. From there we validate through a proof of concept before expanding into production, which lowers early risk and gives stakeholders evidence before the larger investment. 

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Let's identify the opportunities worth pursuing, define the right architecture before anything gets built, and map a practical path to production.

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Architecture decision · Data readiness · Deployment constraint · Practical next step