Deploy AI agents on data you can trust

The best time to deploy AI is when you have a clear business problem or workflow you can improve via automation. Based on our experience, AI tends to work best in processes that are already well-defined, repeatable, and follow a clear pattern because the possible decisions and outcomes are easier to identify, automate, and validate, even when some steps still require human approval or intervention. Most of our clients come to us with one of two situations:

• A clear need to automate a process. In those cases, our AI engineers break down each step, review the systems and data involved, define the technical approach, and turn the idea into a practical automation plan. We look at constraints like budget, timeline, security, integrations, and how much a human needs to get involved in the process.
• A rough idea of what to automate, but no clear starting point. If so, we help the client map out every workflow, identify bottlenecks, and understand where teams spend time on repetitive or manual work. Our Forward Deployed Engineers have a few discovery sessions 2-3 times a week to understand the pain points and identify processes that are good candidates for automation.

We also remind clients that not every solution needs to be built with AI agents. Oftentimes, our engineers can explore cheaper, simpler, and more effective alternatives such as Robotic Process Automation (RPA), custom software scripts, integrations between existing systems, or a combination of third-party tools. Our AI implementation recommendations explain the controls and technical decisions involved.

This is a tough question to answer because the cost of running AI agents in production can range from a few dollars per month for a simple workflow to $10,000+ per month for complex systems that process millions of data records. So, the biggest cost driver is usually how efficiently the AI system is architected. Our team has a few tips to keep costs under control:

• Use different models for different tasks. In our experience, cheaper and simpler models can handle the vast majority of tasks. We reserve more capable, more expensive models, such as Fable, for the smaller number of tasks that require deeper reasoning or more complex decision-making.
• Build the system in a modular way. Rather than having one model do everything, we break the workflow into smaller pieces and choose the best model or tool for each step. That makes the system easier to control, easier to improve, and much more cost-efficient because you are not paying premium-model prices across the entire workflow. Beyond cost, it also improves governance, since you can segregate security and access by module.
• Control token usage from the beginning. Much of the unnecessary AI spend comes from sending too much context back and forth. We can set token limits, pass only the information each agent needs, avoid carrying full conversation histories when they aren't necessary, and use structured outputs instead of long responses for simple tasks.
• Use orchestration to decide how tasks are executed. In more complex agentic systems, our team prefers using orchestrators like LangGraph to coordinate tasks between different agents. The orchestrator decides which agent handles each task, what information it receives, how to interpret the result, and what happens next. A good analogy is a manager assigning different jobs to specialized workers instead of asking one person to handle the entire process.
• Keep optimizing after deployment. Usually, the first version of an AI system isn't fully optimized for cost or efficiency. Once deployed, we can see which tasks consume the most tokens, where expensive models are overused, and where cheaper models or tools could do the same job. Over time, we keep adjusting the routing, token limits, and model selection to find the right balance between cost, speed, and output quality.

We can also reduce costs by using cheaper third-party tools for specific tasks and taking advantage of provider credits where available. The main idea is to avoid using a premium AI model for every step when a simpler and cheaper option can do the job just as well.

Our team believes an AI project is worth implementing when the value it creates through time savings, lower labor costs, increased capacity, or additional revenue is greater than the cost of building and running it. We like to assess the ROI of a project based on:

• How much time can this new AI system give back to the team?
• What higher-value work can people (or other AI agents) do with that time?

Before investing heavily in AI, we strongly recommend starting with a short discovery engagement with one of our AI engineers to determine what can be automated and if the investment makes sense for your business. Once that's clear, we can define ROI against the business metrics or standard KPIs, such as time saved, lower operating costs, increased capacity, faster response times, or additional revenue. We can also propose the most suitable AI engineering team for your company, along with the timeline, implementation cost, and expected ongoing maintenance costs.

We get this question a lot from clients. From our point of view, these three roles often work together, but each focuses on a different part of an AI implementation roadmap. A Data Engineer prepares and manages the data AI systems rely on, an AI Engineer builds the underlying AI systems and architecture, and a Forward Deployed Engineer (FDE) applies those capabilities to specific business problems and helps turn them into working solutions.

• Data Engineer
  • Main focus. Builds and maintains the data infrastructure that makes company data clean, reliable, accessible, and AI-ready.
  • Typical skills. Data pipelines, ETL/ELT, data warehouses, data lakes, SQL, Python, Snowflake, Databricks, AWS, Azure, and GCP.
  • Technical profile. Specialized in data infrastructure, data quality, integrations, and preparing data for analytics and AI.
  • Best fit when. Your data needs cleaning, integration, migration, validation, or preparation before it can reliably support AI.
• AI Engineer
  • Main focus. Builds the AI system itself, including its architecture, models, inference layer, and AI infrastructure.
  • Typical skills. LLM development, RAG, vector databases, fine-tuning, cloud computing, inference systems, and agent frameworks.
  • Technical profile. More specialized in AI architecture, software engineering, model infrastructure, voice AI, and other deeper technical areas.
  • Best fit when. You need custom AI infrastructure, complex RAG systems, model fine-tuning, AI agents, or deeper architecture work.
• Forward Deployed Engineer
  • Main focus. Works directly with the business to identify where AI can create value and quickly turn those opportunities into working solutions.
  • Typical skills. AI integrations, automation, APIs, workflow mapping, rapid prototyping, system integrations, and stakeholder communication.
  • Technical profile. Still highly technical, but more focused on quickly building, testing, and iterating solutions alongside the client.
  • Best fit when. You want to automate a business workflow and need someone to define, prototype, build, and integrate the solution.

We assign Data Engineers to clean, structure, and validate data across each ETL and ELT pipeline. Our team always works from the premise of giving agents reliable data and business context so they can produce outputs your organization can trust when creating reports, making decisions based on AI recommendations, and automating workflows. At Ewents, we are strict about using clean data because AI-driven applications rely on it. If the underlying data is incomplete, inconsistent, or incorrect, an employee or even another agent may end up working with information that one can’t trust. So, we usually focus on:

• Keeping identifiers and formats consistent. Customers, products, transactions, and other records should follow the same naming conventions and formats across systems so agents can connect information correctly.
• Cleaning and validating incoming data. We check for duplicates, missing values, incorrect data types, stale records, and other issues before the data reaches downstream applications.
• Testing transformations and business rules. We validate that ETL transformations are working as expected and that the relationships and business logic behind the data are correct.
• Automating data quality checks and monitoring. We can implement automated data quality checks for completeness, duplicates, relationships, data types, and business rules, while monitoring for failed loads, stale data, and unexpected changes.

Surprisingly, our team has also found it useful to use AI to help identify anomalies, flag unusual data patterns, and suggest possible corrections. That said, any significant changes to the data should still go through defined validation checks and be reviewed by an experienced engineer before being applied in production.

Most of our AI projects take around 2 to 3 weeks to reach a first useful iteration. That said, the timeline depends a lot on what we are trying to achieve, the available budget, and the tools we can use. A typical timeline for deploying a first AI implementation looks like this:

1. First few days: build the initial version. For example, if our team is developing a voice AI agent for a bank call center, our engineers can combine existing tools such as Retell, ElevenLabs, or LiveKit with the client’s databases and internal systems to place calls, interact with customers, record outcomes, and trigger the next step.
2. Next, connect the full workflow. Today, tools such as Claude Code or Codex make it much faster for our team to build the backend that supports AI applications. The bigger challenge is usually connecting the models, APIs, databases, agents, and third-party tools so everything works reliably from end to end.
3. Then, test and improve it. Once the first version is working, our team runs evaluations, or evals, to test how the agent behaves across different scenarios. We combine that with QA and data validation to catch mistakes, test edge cases, and ensure the workflow produces the expected results.
4. Finally, optimize it in a real environment. Our engineers usually spend a few more days testing and improving the system in production or a production-like environment. This is where unexpected user behavior, integration issues, and edge cases tend to appear, and we use those findings to improve reliability before scaling the solution further.

So, while our team can deploy a prototype in as little as a few days, a more realistic timeline is 2 to 3 weeks, assuming the outcome is a PoC that is tested and ready for real-world use.