# Techwards — AI & Data Engineering Agency > Techwards is a US-based software and AI engineering agency headquartered in San Jose, California (founded 2020). We deliver data engineering services, AI agent development, agentic workflow automation, and custom software for US businesses. Contact: info@techwards.co | +1 (279) 201-9344 | 6469 Almaden Expy, San Jose, CA 95120. ## Key pages - [Homepage](https://techwards.co/) - [Data Engineering Services](https://techwards.co/services/data-engineering-services/) - [AI Agent Development](https://techwards.co/services/ai-agents-workflows-agentic-os/) - [AI Adoption Services](https://techwards.co/services/ai-adoption/) - [Full Stack Development](https://techwards.co/services/full-stack-development/) - [About Us](https://techwards.co/about/) - [Blog](https://techwards.co/blog/) - [Contact](https://techwards.co/contact/) --- # Data Engineering Services URL: https://techwards.co/services/data-engineering-services/ Primary keyword: data engineering services Secondary keywords: data engineering consulting, data integration services, data engineering company, data integration company Long-tail keywords: etl services, etl development, data engineering agency ## Summary Techwards delivers data engineering services that turn scattered, inconsistent data into a single reliable foundation for reporting, automation, and AI. We build data pipelines, ETL systems, data warehouses, data integration systems, and AI-ready data foundations for US businesses. US-based team headquartered in San Jose, California. ## Why Unreliable Data Is Holding Your Business Back Most companies don't have a data shortage. They have a data trust problem. Systems don't talk, numbers conflict, and every initiative starts with manual reconciliation. According to MuleSoft's 2026 Connectivity Benchmark Report, which surveyed over 1,050 IT leaders globally, only 27% of the average enterprise's applications are actually connected to each other. That same report found 82% of IT leaders cite data integration as one of the biggest barriers to using AI effectively. A 2025 IBM Institute for Business Value study found that more than a quarter of organizations lose over $5 million a year to poor data quality, with 7% losing upwards of $25 million. Forty-three percent of COOs surveyed now rank data quality as their single biggest data priority. This is the gap data engineering closes: turning disconnected systems into a foundation your business can actually build on. ## What Our Data Engineering Services Cover We build the full data layer your business runs on, from the pipelines that move data to the governance that keeps it trustworthy. ### Data Pipeline Development We design and build automated pipelines that extract, clean, and move data from your CRMs, ERPs, databases, and internal tools into systems your team can actually use. Every pipeline is built to run unattended, fail loudly when something breaks, and scale as your data volume grows. ### Data Integration & ETL Our data integration services and ETL development connect CRMs, ERPs, and operational tools with ETL/ELT workflows so information flows automatically and consistently. Our ETL services handle extraction, transformation, and loading across legacy databases, modern SaaS tools, APIs, and everything in between. ### Data Warehouse & Architecture We design and implement cloud data warehouses built around how your business actually reports and analyzes data, not a generic template. The result is a single structured environment your BI tools, analysts, and AI systems can all draw from. ### Real-Time Data Pipelines When daily or hourly batches are not fast enough, we build streaming pipelines that move data the moment it is created, powering live dashboards, instant alerts, and operational decisions that cannot wait for tomorrow's report. ### Data Governance & Quality We put validation, access controls, and monitoring in place so bad data gets caught before it reaches a dashboard or a decision. Good governance is not a separate project bolted on at the end — it is built into every pipeline from day one. ### Legacy Data Modernization We move data out of aging databases and brittle, undocumented systems into modern, scalable infrastructure, without disrupting the operations that depend on them while the migration happens. ### AI-Ready Data Foundations Gartner predicts that up to 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025. AI agents are only as good as the data they can access. We structure, clean, and govern your data so it is ready to power AI agents, predictive models, and automation, not just dashboards. ## Data Engineering Consulting Not every engagement starts with a build. Sometimes you need a second opinion on your data architecture, a clear-eyed assessment of why your pipelines keep breaking, or a roadmap before you commit budget to a full implementation. That is what our data engineering consulting is for. We start by understanding how your data actually moves today: where it lives, where it breaks, where teams have built workarounds because nobody fixed the underlying problem. From there, we give you a concrete plan — what to build, what to fix, what to leave alone, and what it will cost — before any development work begins. Your roadmap before any code: 1. What to build — new pipelines and integrations worth the investment 2. What to fix — where pipelines break and why they keep breaking 3. What to leave alone — what already works and should not be touched 4. What it will cost — a concrete budget and timeline, fixed up front ## Data Integration Services If your CRM, ERP, support platform, and finance tools all tell a slightly different story about the same customer, you have an integration problem, not a reporting problem. Our data integration services connect these systems so information moves automatically and consistently, instead of depending on someone remembering to export a CSV every Monday. We work with both modern API-first platforms and the older systems many businesses still depend on, building integrations that are resilient to API changes, documented clearly enough that your team is not dependent on us forever, and monitored so a silent failure does not go unnoticed for weeks. As a dedicated data integration company, this is the work we do every day, not a side capability bolted onto a broader software contract. ## Why Work With a Dedicated Data Engineering Company Most businesses reach a point where they need real data engineering capability and have to decide: build the team internally, or bring in a data engineering agency that already has it. Hiring internally means recruiting for a skill set that is genuinely scarce, then carrying that headcount permanently, even after the initial build is done and the ongoing work is closer to maintenance than full-time engineering. Working with a dedicated data engineering company gets you a team that has solved this exact problem at other companies, moving faster because they are not learning on your dime, and scaling up or down as the work requires instead of locking you into permanent headcount. Data engineering and integration is a core practice at Techwards, staffed by engineers who do this full-time, backed by a process refined across real client engagements. You get senior-level data engineering without the 4-to-6-month hiring cycle, and without carrying the cost of a full-time team once the heavy lifting is done. ## Our Process 1. Assess — We map your current data sources, systems, and pain points, and identify where the biggest risks and opportunities are. Deliverables: Data landscape map, quality assessment, readiness report. 2. Design — We design the target architecture, pipelines, and governance model that fit your reporting needs, data volume, and future plans. Deliverables: Architecture plan, data flow design, governance model. 3. Build — We build the pipelines, integrations, and infrastructure, writing clean, documented, maintainable systems from day one. Deliverables: Working pipelines, integration code, technical documentation. 4. Validate — We test thoroughly: data accuracy, pipeline reliability, performance under real load, before anything goes live. Deliverables: Validation report, quality checks, performance benchmarks. 5. Launch — We deploy into production, connect to your live systems, and confirm everything runs as designed under real conditions. Deliverables: Production deployment, monitoring setup, handoff documentation. 6. Monitor & Scale — We set up ongoing monitoring and alerting, and stay available to scale the system as your data volume and needs grow. Deliverables: Monitoring dashboard, alerting rules, scaling roadmap. ## Industries We Serve Healthcare: We build HIPAA-aware data pipelines that unify patient, claims, and operational data across EHRs and other clinical systems, without disrupting care delivery in the process. Fintech: We design data infrastructure built for accuracy and auditability, where a reporting error is not just inconvenient but a compliance risk. Ecommerce: We connect storefronts, inventory, fulfillment, and marketing data into a single pipeline, so the question "what is actually selling" has one trustworthy answer. ## Frequently Asked Questions Q: What is the difference between data engineering and data integration? A: Data engineering is the broader discipline, designing and building the pipelines, infrastructure, and architecture your data runs on. Data integration is one part of that work: connecting separate systems so data moves between them automatically. Most engagements involve both. Q: How much do data engineering services cost? A: It depends on scope, the number of systems involved, your current data maturity, and whether you need a full build or a targeted fix. We give every client a clear cost estimate after the initial assessment, not a guess upfront. Q: Do you offer data engineering consulting without a full build? A: Yes. Some clients only need an assessment and roadmap, not a development engagement. We are upfront about which one your situation actually needs. Q: What is the difference between ETL development and ETL services? A: ETL development refers to building a specific pipeline or integration. ETL services is the broader, ongoing offering — development, monitoring, and maintenance together. We offer both, depending on what stage you are at. Q: Do you handle real-time data pipelines? A: Yes. When batch processing is not fast enough, we build streaming pipelines for live dashboards, instant alerts, and time-sensitive operational decisions. ## Sources - MuleSoft 2026 Connectivity Benchmark Report: https://blogs.mulesoft.com/agentic-perspectives/connectivity-benchmark-report/ - IBM Institute for Business Value, 2025 CDO Study: https://www.ibm.com/think/insights/cost-of-poor-data-quality - Gartner press release, Aug 26 2025: https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026 --- # AI Agent Development Company URL: https://techwards.co/services/ai-agents-workflows-agentic-os/ Primary keyword: ai agent development company Secondary keywords: ai agent builder, agentic os, enterprise ai agents Long-tail keywords: hire ai agent developers, agentic ai consulting, agentic workflow automation, ai agent orchestration ## Summary Techwards is an AI agent development company that builds agents which actually plug into your business, not isolated demos that never leave the sandbox. Whether you need an AI agent builder approach for a focused use case or a fully custom AI agent development engagement across multiple workflows, our team designs, builds, and governs agents that take real action inside your CRM, ERP, support tools, and internal systems. US-based team headquartered in San Jose, California. ## What Is an AI Agent? An AI agent is software that perceives its environment, makes decisions, and takes actions to achieve a defined goal — without waiting to be told each step. Unlike a chatbot that responds to input, an agent acts on it. AI agents connect to your real systems — CRMs, ERPs, databases, and APIs — and execute multi-step tasks autonomously. They can retrieve data, make decisions within defined boundaries, trigger workflows, and escalate to a human when needed. AI agent development services turn this capability into production-ready systems. Built for your specific workflows, data, and governance requirements. ## Why Most AI Agent Projects Never Reach Production Everyone is building AI agent pilots right now. Almost nobody is running them in production six months later. The gap is not ambition — it is infrastructure. Gartner predicts that up to 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025. Gartner also predicts that over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls. Deloitte's 2026 State of AI in the Enterprise report found that 85% of companies expect to customize agents to fit their business, but only 21% report having a mature model for governing those agents once they are live. This is the actual job of AI agent development: not making an agent that can talk, but making one that can be trusted to act, inside systems that already matter to your business. ## Types of AI Agents We Build ### Workflow & Process Automation Agents These agents handle repetitive, rule-based, document-heavy work — invoice validation, CRM updates, approval routing, internal reporting — so your team stops doing the parts of the job that were never a good use of a person's time. Unlike traditional automation, these agents can interpret context and handle exceptions instead of failing silently. ### Conversational & Support Agents Built for customer support, internal helpdesk, and lead qualification, these agents answer questions, retrieve context from your actual systems, and escalate to a human exactly when escalation is the right call — not after a frustrating loop. The difference between a good conversational agent and a chatbot is that this one knows when to stop talking. ### Knowledge & Document Agents These agents search, summarize, and extract information from contracts, policies, reports, and internal knowledge bases — turning "ask someone who has been here five years" into something anyone on the team can do in seconds. ### Predictive & Decision Agents Using your historical and real-time data, these agents forecast outcomes, flag anomalies, and recommend next actions for teams making decisions that used to depend on gut feeling and a half-updated spreadsheet. The agent does not replace the decision-maker — it makes sure they are working from the right information. ### Multi-Agent & Orchestrator Systems For workflows too complex for a single agent, we design systems where specialized agents each own a piece of the job — one retrieves, one validates, one drafts, one routes for approval — coordinated by an orchestrator that keeps the whole process moving. ## AI Agent Builder vs. Custom Development An AI agent builder is the right tool when you need to validate an idea quickly and the stakes of getting it slightly wrong are low. It is fast, cheap, and requires almost no engineering investment. Custom AI agent development is the right call when the agent needs to write to a production database, handle sensitive customer data, integrate with systems that do not have a pre-built connector, or operate reliably enough that a failure actually costs you something. The line between the two is not always obvious. Part of how we start every engagement is helping you figure out honestly which one you actually need. Sometimes that means recommending you start with a builder platform and come back to us when you outgrow it. ## Agentic OS & Workflow Orchestration A single working agent is a pilot. Multiple agents operating across your business without a shared system for visibility and control is a liability waiting to happen. Deloitte found that 85% of companies expect to customize agents to fit their business, but only 21% have a mature governance model. Gartner predicts that by 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance gaps identified only after production incidents. We help set up the operating layer that lets agentic workflow automation scale safely: an agent registry that tracks who owns each agent and what it is allowed to touch, defined approval paths for sensitive actions, and monitoring that logs what each agent did, what data it accessed, and what it changed. ## Why Work With a Dedicated AI Agent Development Company Building a single AI agent demo is genuinely not that hard anymore. Building one that survives contact with your actual systems, your actual data quality, and your actual compliance requirements is a different problem entirely. A dedicated AI agent development company has already solved the parts that do not show up in a demo. Authentication across five different internal systems. What happens when the underlying model changes. How to build in the human approval steps your legal team will ask for eventually. For enterprise teams, more systems and more stakeholders means "we will figure it out as we go" gets expensive fast. ## Build vs. Hire: Do You Need an Agency or a Developer? Hiring a developer directly is right for a single, well-scoped task with a clear internal owner and someone technical already on staff. Working with an agency is right when you need strategy alongside development, when no one internally has built a production agent before, or when the work needs to include governance, integration, and a process that holds up past the first agent. The meaningful cost difference between the two options usually disappears once you account for the mistakes a first-time implementation makes that an experienced team would not. ## Our Process 1. Discover — We identify the highest-value use case, check data and system readiness, and define what success looks like before any code gets written. Deliverables: Use case brief, feasibility assessment, KPI definition. 2. Design — We design the agent's architecture, decide what it can access, and define approval paths before it is allowed to touch anything sensitive. Deliverables: Agent architecture, integration plan, access and approval design. 3. Build — We build the agent, the integrations it depends on, and the evaluation framework that tells us whether it is actually working. Deliverables: Working agent, integration code, evaluation framework. 4. Validate — We test the agent against real scenarios, including the ones designed to break it, before it ever touches production data. Deliverables: Test results, edge-case report, performance benchmarks. 5. Deploy — We connect the agent to your live systems and roll it out in a controlled environment with real users and real oversight. Deliverables: Production deployment, monitoring setup, access controls. 6. Monitor & Scale — We track performance, accuracy, and cost, then use that data to decide what the next agent should be. Deliverables: Performance dashboard, optimization plan, next-agent roadmap. ## Industries We Serve Financial Services: We build agents for reporting, fraud detection, and customer workflows, with the audit trail and approval steps that regulated industries actually require. Financial services agents need to be right, documented, and explainable — not just fast. Healthcare: We design HIPAA-aware agents for administrative work, documentation, and patient communication, without putting clinical judgment in the hands of a model. The governance layer matters here more than anywhere else, and we build it in from the start. Software & Technology: We build agents that handle support, internal tooling, and DevOps workflows for teams that move fast and need automation that keeps up without breaking things. ## Frequently Asked Questions Q: What is the difference between an AI agent and a chatbot? A: A chatbot responds to input. An AI agent acts on it. Chatbots answer questions within a conversation window. AI agents take multi-step actions inside real systems — updating a CRM, routing an approval, triggering a workflow — without waiting to be asked each time. Most AI agent products on the market today are actually chatbots with better marketing. Q: What is the difference between an AI agent builder platform and custom AI agent development? A: An AI agent builder is a no-code or low-code tool for assembling a basic agent quickly. It works well for simple, self-contained use cases. Custom AI agent development means building an agent specifically architected for your systems, data, authentication requirements, and workflows — with integrations, approval paths, and monitoring a builder platform typically cannot support. Most teams start with a builder and move to custom when they hit the first integration wall. Q: Why do so many AI agent projects fail before reaching production? A: Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. Projects succeed in a demo environment because demos do not touch real authentication, real data quality problems, or real compliance requirements. When they do, the architecture that worked in the sandbox does not hold. Q: Do you offer AI agent consulting without committing to a full build? A: Yes. Some teams need a scoped discovery sprint and a clear recommendation more than they need a development engagement. We will tell you honestly which one your situation calls for — including whether you are better served starting with a builder platform and returning to us when you have outgrown it. Q: What is an Agentic OS, and does my company need one? A: An Agentic OS is the governance and orchestration layer that gives you visibility and control over multiple AI agents — a registry of what each agent owns and can access, defined approval paths for sensitive actions, and monitoring that logs every action an agent takes. You need one the moment you have more than one agent in production. Without it, the second agent exposes the governance gaps the first agent happened not to trigger. Q: What is the difference between agentic workflow automation and traditional automation? A: Traditional automation follows fixed, predefined rules — if this, then that, with no ability to interpret context or handle exceptions. Agentic workflow automation uses AI agents that can read context, make decisions within defined boundaries, and adapt to situations the original rules did not anticipate — like escalating an edge case to a human instead of failing silently. Q: Should we hire AI agent developers directly or work with an agency? A: Hiring a developer directly is right for a single, well-scoped task with a clear internal owner and someone technical already on staff to manage the engagement. Working with an agency is right when you need strategy alongside development, when no one internally has built a production agent before, or when the work needs to include governance, integration, and a handoff process that holds up past the first agent. The meaningful cost difference usually disappears once you account for the mistakes a first-time implementation makes that an experienced team would not. ## Sources - Gartner press release, Aug 26 2025: https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026 - Gartner press release, Jun 25 2025: https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027 - Deloitte State of AI in the Enterprise 2026: https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html - Gartner press release, May 26 2026: https://www.gartner.com/en/newsroom/press-releases/2026-05-26-gartner-says-applying-uniform-governance-across-ai-agents-will-lead-to-enterprise-ai-agent-failure --- # About Techwards URL: https://techwards.co/about/ Techwards is a US-based software and AI engineering agency founded in 2020, headquartered in San Jose, California. We deliver data engineering services, AI agent development, agentic workflow automation, full stack development, and AI adoption services for US businesses ranging from growth-stage startups to mid-market enterprises. Our team operates across the US and includes an offshore delivery team. We work as an extension of our clients' engineering organizations — not as a vendor at arm's length. Contact: info@techwards.co | +1 (279) 201-9344 Address: 6469 Almaden Expy Ste 10, San Jose, CA 95120, US LinkedIn: https://www.linkedin.com/company/techwards --- # Contact URL: https://techwards.co/contact/ Get in touch with Techwards to discuss data engineering services, AI agent development, or any other engineering need. Email: info@techwards.co Phone: +1 (279) 201-9344 Address: 6469 Almaden Expy Ste 10, San Jose, CA 95120, US