AI Systems
40%
higher close rates
- 18 dayssales cycle, down from 45
- $1.2Mstalled pipeline recovered
NexusCRM
AI-native CRM decision system
Lead scoring, outreach drafting, and pipeline risk detection were rebuilt around AI decision support.
AI Agents & App Integrations
Agents that do useful work in your tools.
Build assistants and agents that retrieve knowledge, draft responses and execute permitted tasks. Connect them to your stack with evaluation, audit trails and escalation paths.
- Model-agnostic architecture
- Human review on edge cases
- Connected to your tools
AI IN THE WORKFLOW
A support request becomes a grounded answer and a tracked task.
An example of what we can scope with your team. The tools and automation boundaries follow your workflow.
Find the context
Retrieve permitted knowledge and the relevant customer history.
Prepare the action
Draft an answer or tool call and validate required details.
Apply the boundary
Execute allowed actions; escalate sensitive or uncertain requests.
What you take away
An integrated AI workflow with evaluation cases and tool permissions.
Depending on fit: Copilot Studio · Power Platform · model APIs · MCP
Choose a defined project or ongoing delivery. Engineering Capacity is one option when its scope fits. Scope and price are agreed first.
Organizations we’ve worked with
- Airbnb
- TheAMLab
- Hood
- NAMA
- Klaro
- GoBoon
- Studio Arlington
What is holding your AI feature back?
These are the patterns we see most often when AI pilots fail to scale beyond a demo.
Select your bottlenecks to see possible starting points
Sound familiar?
Choose any that apply. No contact details needed.
Assistants and Action Agents
Both can be useful. Choose grounded assistance or permitted execution according to the task and risk.
AI Tool
AI Agent
AI Tool
Answers questions when asked
AI Agent
Monitors, decides, and acts proactively
AI Tool
Passive — waits for user input
AI Agent
Autonomous — runs on events and schedules
AI Tool
Needs a human to initiate every step
AI Agent
Monitors systems and triggers on conditions
AI Tool
Delivers output to a chat interface
AI Agent
Executes actions in your actual tools and workflows
AI Tool
Requires manual supervision
AI Agent
Escalates for policy, permissions, uncertainty and required approvals
The difference is not the model. It's the architecture around it.
The Architecture of an AI System
Every agent we build follows this five-layer structure — regardless of the use case.
- 01
Inputs
Data from your systems: emails, tickets, CRM records, APIs, databases, documents
- 02
Reasoning
The model evaluates context using your business rules, not just generic instructions
- 03
Decision
Route, classify, approve, flag, or reject — based on confidence and defined thresholds
- 04
Action
Execute permitted actions after required approvals: update records, send messages, assign tasks, create tickets
- 05
Feedback
Audit trail generated, confidence logged, edge cases surfaced for human review
The system acts. You audit. You approve edge cases. You don't manually handle the volume.
Four Types of AI Agents We Build
Choose the starting point based on where human judgment is currently the bottleneck.
Monitoring Agents
Watch data streams, inboxes, or system events and alert when something requires attention — before it becomes a problem.
SLA breach detectionAnomaly flagging in operations dataInventory threshold alerts
Decision Agents
Evaluate incoming information and apply your classification or routing logic — without a human reviewing every case.
Lead scoring and routingSupport ticket priority classificationDocument category tagging
Action Agents
Take the next step automatically after a decision: create tasks, update records, send communications, trigger downstream workflows.
Auto-response generationCRM and ERP record updatesMulti-step process initiation
Learning Agents
Surface patterns from past decisions and outcomes — identifying where the current system could be calibrated or improved.
Decision accuracy reportingEdge case pattern detectionModel performance monitoring
Where Companies Use AI Agents
Before
Sales ops: reps manually qualify and route every lead
After
Agent scores, routes, and triggers follow-up sequences within seconds
Before
Support: team reads and categorizes every incoming ticket
After
Agent classifies, prioritizes, and auto-assigns — humans handle edge cases
Before
Operations: managers spend hours on status requests and updates
After
Agent monitors pipelines and sends proactive status updates on schedule
Before
Compliance: staff manually review documents for risk signals
After
Agent flags high-risk clauses and routes for human review automatically
Before
Knowledge: analysts spend hours extracting insights from reports
After
Agent reads, summarizes, and surfaces key findings with source citations
Is this the right next step for your AI project?
Ready to scope a feature
- Operations with high-volume, repeatable decision workflows
- Teams drowning in intake, classification, or routing tasks
- Businesses that have tried basic automation but need AI-level judgment for complex decisions
- Companies with defined escalation paths for when automation should defer to humans
Clarify these first
- Businesses that haven't defined what decisions they want to automate
- Teams not ready to provide feedback loops for agent calibration
- Use cases requiring creative judgment or interpersonal nuance
- Projects without access to the sources or tools required for their specific use case
Next step
Map the first AI system worth deploying
A 30-minute AI opportunity session. We identify which decisions in your operation are high-volume, rule-based, and ready for an agent to handle.
Book a callWhat we build
AI System Engagements
We design AI systems that sit inside real operations, follow business rules, and escalate only when human judgment is actually required.
- 01
Triage at the edge of the operation
Read inbound tickets, emails, forms, and documents, then classify, enrich, and route them without manual sorting.
- 02
Confidence-scored recommendations
Score leads, summarize cases, surface risk signals, and recommend next actions with visible confidence thresholds and audit trails.
- 03
Systems that do the next step
Push updates into CRM, ticketing, ERP, messaging, and internal tools so AI outputs turn into completed work, not another dashboard.
- 04
Human review where it matters
Log confidence, detect drift, and route edge cases to the right operator so the system improves without becoming opaque.
How we deliver
Deployment Model
Most AI system engagements move from operational audit to supervised production in four stages.
- 01
Decision Audit
We map the decision workflow, define confidence thresholds, and identify what data sources the agent needs access to.
- 02
Shadow Mode
A working agent is deployed in shadow mode. It makes decisions, but outputs are reviewed before execution.
- 03
Supervised Rollout
The agent runs live with a human-in-the-loop for edge cases. Calibration happens based on real decision outcomes.
- 04
Operate & Improve
The agent operates autonomously within defined confidence bounds. Escalations are logged and reviewed on a cadence.
In production
Systems already in production
Explore AI and integration work inside real business systems.
WalmartAI Systems
Device failure detection with machine learning
92% prediction accuracy and 65% lower device downtime.
Reel CreatorAI Systems
AI video generation pipeline
100,000+ reels generated while production time dropped by 85%.
A clear starting point
Choose the work you need now.
Choose an assistant, decision aid or action agent based on the task. A defined feature can be scoped as a project; Engineering Capacity suits an evolving implementation backlog.
- Knowledge assistant
- Recommendations inside a product
- Agents with permitted actions
Agree the finish line first
A working feature, representative evaluation cases, access boundaries and a supervised release path.
Model usage, data preparation and ongoing evaluation are agreed explicitly.
Scope my AI featureSomething already broken? Explore System Rescue.
By industry
Built around your sector
Explore the systems and operating needs that shape delivery in your sector.
Explore all industriesBEFORE YOU GET STARTED
Frequently asked questions
Scope, delivery and what happens next. The details that help you decide.
Do we need an autonomous agent?
Not necessarily. Search and grounded answers may be enough. Recommendations can stay with a human decision-maker; action agents are appropriate only where permissions, checks and recovery are defined.
Do we need a large training dataset?
Requirements depend on the task. A retrieval assistant needs usable source material and access rules; a tool-based agent needs reliable APIs. Custom prediction or fine-tuning may need labelled historical data.
How do you decide if the feature is ready?
We agree representative cases, unacceptable errors, response time and cost limits. Evaluation and supervised testing precede release, with a fallback when the system cannot complete a task safely.
Can the model access confidential information?
Only the data and tools approved for the use case. We review provider processing, retention, permissions and logging requirements before connecting production sources.
What actions need human approval?
Policy restrictions, missing permissions, high-impact actions and uncertain outputs can all require review. A confidence score alone is not permission to act.
Who pays for model usage and owns the implementation?
Provider usage and third-party licences are identified separately. The agreement defines source-code handover, account ownership and any reusable components before implementation.
What happens when a model changes?
Updates should be evaluated against the agreed cases before release. Ongoing monitoring, evaluation and maintenance are included only under an agreed support scope.
Start here
Start with the decision AI should help with
We map one workflow where AI can support a decision or take a permitted action, set its guardrails, and show you the first system to build.
20–30 minutes · No preparation needed · Free