Build Software
Build new production software end to end — from first architecture to platforms, applications, data systems, and AI at the core.
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6sense HQ builds AI-enabled workflows, agents, integrations, and data automation for businesses moving beyond proof-of-concept automation.
Each solution is engineered around the process it supports, including system integrations, business rules, validation, fallback handling, monitoring, and human review where required.
The result is automation designed to operate reliably with real business data and production systems.
Build new production software end to end — from first architecture to platforms, applications, data systems, and AI at the core.
How we build?Fix the integration failures, recurring bugs, and production instability — then make the system reliable enough to build on again.
How we fix?Grow and evolve your running system — new capabilities, more load, more reliability — with a senior team that stays on it, so nothing destabilizes while it scales.
How we maintain?
Effective automation starts with understanding the workflow, the systems involved, and the decisions the process needs to make.
Some steps are well suited to AI, including document extraction, classification, summarization, and recommendation. Others are better handled through standard software logic, APIs, or predefined business rules.
The architecture should reflect the level of accuracy, control, and reliability required by the process.
Where the consequences of an incorrect result are higher, the workflow can include validation, confidence thresholds, exception handling, approval steps, or human review.
AI becomes one component of the system rather than the entire system.
One of our automation projects involved a multi-step process where information was collected, reviewed, processed, and entered manually across several systems.
The workflow was redesigned so repetitive extraction, routing, and validation could run automatically, while human review remained in the parts of the process that still required judgment.
AI Engine | New York, US | Build
Built ETL pipelines across all 50 US states.
Unified well, production, land, and regulatory data in one platform.
Added new states without rebuilding the system.
AI Automation
Workflow automation, intelligent routing, data extraction, classification
Process Automation
Internal platforms, billing workflows, document processing, operational workflows
AI Agent Development
Task-specific agents, internal copilots, data retrieval, system actions
Web Applications
Operational dashboards, internal tools, customer portals, data-driven applications
We automate multi-step processes across applications, teams, and data sources.
The solution can include workflow logic, queues, approval steps, AI components, system integrations, and exception handling.
Information from forms, emails, PDFs, invoices, and other documents can be extracted, classified, validated, and passed into downstream systems. Uncertain results can be routed for review before further processing.
Task-specific agents can be built around defined responsibilities, permitted data, system access, and business rules. Typical applications include information retrieval, research, processing, routing, and internal operational support.
Automation can connect with CRM, ERP, support platforms, billing systems, databases, internal applications, and external APIs.
Production workflows can include logging, retries, alerts, validation, and failure handling so operational problems remain visible.
Model usage can be monitored and limited where required, while standard software logic can handle tasks that do not need AI.
Repetitive Data Handling
Teams repeatedly read, enter, check, or transfer the same types of information between systems.
High-Volume Processing
A manual workflow becomes difficult to manage as the number of requests, records, or transactions grows.
Document-Heavy Workflows
Forms, emails, PDFs, and other unstructured inputs need to be reviewed before the next step can begin.
Manual Triage
Requests, tickets, records, or cases are categorized and routed by people before work starts.
Reporting & Reconciliation
Information has to be collected from several sources before it can be used for reporting or decision-making.
Existing Automation Needs Improvement
A workflow already exists but requires stronger validation, monitoring, exception handling, or integration.
A workflow that performs well in testing may still require additional engineering before it can support day-to-day operations.
Business data may arrive incomplete, duplicated, delayed, inconsistent, or in an unexpected format. External services can become unavailable, APIs can change, and downstream systems can reject otherwise valid requests.
Production automation therefore needs clear handling for these situations.
Depending on the workflow, that may include input validation, retry logic, fallback behaviour, exception queues, human review, monitoring, and controlled failure states.
The exact controls depend on how the automation is used and the impact of an incorrect or incomplete result.
Document Processing
Extract and structure information from forms, invoices, PDFs, emails, and other business documents.
Support Triage
Classify incoming requests, add context, and route them to the relevant workflow or team.
Data Extraction & Reconciliation
Collect information across systems, compare records, identify mismatches, and surface exceptions.
Internal AI Assistants
Provide employees with controlled access to internal information and defined operational tasks.
Reporting Automation
Collect and process the information required for operational and management reporting.
Review & Approval Workflows
Automate routine processing while retaining human approval where judgment or accountability is required.
Automation Hardening
Improve an existing automation by strengthening its integrations, validation, monitoring, and exception handling.
Not every result should continue through a workflow automatically.
Low-confidence outputs can be sent for review. Failed integrations can trigger retries or alerts. Deterministic rules can replace AI where the answer should always follow a fixed condition.
Higher-impact actions can also require approval before completion.
These controls are defined according to the business rules and risk level of the process rather than applied as a standard template.
If you have a manual workflow, an existing automation that needs improvement, or an AI capability that needs to operate inside a production system, we can review the process and technical requirements with you.
A 30-minute conversation with our CEO and CTO can help clarify the systems involved, where AI is useful, and what the first engineering step should be.
