Services · AI Engineering
A forward deployed engineer and an AI pod build agents inside the systems you use. AI prepares the work, and your team approves it.
Grok
Llama
Nemotron
GemmaEach answer is checked, and the cost of every task is logged.
AI workflows
Each workflow uses the same scoring, templates and controls. AI prepares the work, and your team approves it.
Prepares each sales order in SAP for the order desk to approve.
Prepares supplier date changes in SAP for the buyer to approve.
Flags at-risk equipment and drafts the work order for the planner.
Reads each referral and prepares the patient record for intake to confirm.
Drafts the next step on each account for the collector to approve.
Explains portfolio performance and drafts reports for finance to approve.
How we build
Six criteria decide which use cases go first, and which agent and model each needs.
We rate each AI idea from 1 to 5 on six criteria. Higher is better on every criterion, so lower risk and cost score higher.
Hours saved, revenue gained or risk reduced.
Whether the data is trusted, current and easy to reach.
The systems the agent must read from and write to. See the application paths
What happens if the agent gets it wrong.
How much reasoning the task needs, and how often it runs.
What it will cost to run at full volume.
The scores set its priority, agent type, model and size:
Each use case gets one of three agent types, based on its risk.
Answers questions from approved sources, read only, with sources cited.
Takes a step in a process, and a person on your team approves it.
Makes a call within limits your team sets, with a person reviewing exceptions. Never used for decisions that affect a patient, a customer or a loan.
The top scores become the first agents. Each new agent is built faster than the one before.
Safety limits, identity and the actions an agent may never take, agreed with your team.
We build two real agents first, and turn them into templates for the rest.
Every later use case follows the same loop, with scope and timeline agreed first.
Your team sets accuracy and safety targets for each agent before the build. We test against them, and the agent goes live only when it meets them.
For your first use case, who on your side signs off?
Models and applications
The model sets an agent’s cost, where your data goes and whether risk approves it. Most programs use both frontier and open models.
Claude, GPT, Gemini and Grok, through Azure AI Foundry, Amazon Bedrock, Google Vertex AI, Databricks and Snowflake.
Llama, Mistral, Nemotron and Gemma, through Databricks, NVIDIA NIM, Amazon Bedrock and Vertex AI, or on your own GPUs.
Tools we build with
Model Context Protocol
Agent2Agent
LangGraph
Amazon Bedrock AgentCore
Azure AI Foundry Agent Service
Google Agent Development Kit
Databricks Mosaic AI
Agents need safe access to the systems that run your business. We choose one of three paths for each application.
An MCP gateway gives agents controlled access to a system, with no code changes.
For systems that can't handle the volume, or cost more to run than to replace. We replace them in stages while they keep running.
Applications that work well stay in place, kept healthy as the business changes.
Every request is controlled and logged. Each agent can do only what it is allowed to do. We build on the MCP standard, with Azure API Management, Amazon API Gateway or Apigee.
Planning private or on-premises AI? Talk to an architect.
Governance
Each agent works only on approved data and is tested before release. Every action is logged.
Each agent gets its own identity and only the access it needs, with regular reviews.
Microsoft Entra Agent IDAmazon Bedrock AgentCore IdentityGoogle Cloud IAMA register of all AI in use, with risk scores and safety limits. Testing at each release, and audit reports.
Microsoft Purview
Amazon Bedrock GuardrailsGoogle Model Armor
AI Control TowerClient results
Agents scored and built with the same method. See all case studies ›
A live Act agent on SAP and Workday handles routine purchase order approvals. A person approves anything above a set amount, and every step is logged.
Invoice matching, vendor master data, month-end close, purchase-to-pay exceptions and intercompany reconciliation. Each new agent builds on the last.
Five agents live, two small and three medium
How we work
Our teams work inside yours, from the first assessment to running what we build. Most engagements start with a two- to three-week Data Readiness Assessment.
Dedicated teams build your data foundation, on-premises or in your cloud.
A forward deployed engineer and an AI pod put AI to work on your workflows. A person approves key decisions.
We run and support what we build, and improve it as your use grows.
Inside each AI pod. Your forward deployed engineer owns each use case with your business owner. AI engineers, data engineers and governance specialists build and test each agent.
AI needs trusted data, so our data engineers build the foundation first. Data Engineering › Parkar is SOC 2 Type II and ISO 27001 certified.
Get started
Each step stands on its own and ends with something you can use.
Ten questions about your data, systems and teams. You get a score and a clear place to start.
Take the AI Readiness DiagnosticDownload a sample report ›Our data engineers score your data and give you a ranked plan for the first projects.
Book a Data Readiness AssessmentDownload a sample report ›Your AI ideas scored and ranked, with the right agent, model and size for each.
Book AI Use Case PrioritizationDownload a sample report ›Every application scored on value, cost to maintain and readiness for AI.
Book an Application Portfolio AssessmentAlready spending on AI? An AI Cost and Model Review shows where you can lower your AI costs.