Research and market intelligence agents
Search approved web and document sources, collect relevant evidence, compare findings, verify key facts, and deliver structured records or cited research outputs.
AI agent solutions
★★★★★
4.8 / 5
★★★★★
4.8 / 5
Custom AI agents for complex data workflows.
Autonomous agents that navigate the web, parse documents, monitor sources, and run multi-step data workflows. Forage AI builds, runs, and maintains them end to end.
Web + documents
Native agents
Model-agnostic
Architecture
Human approval
Built-in controls
Managed
Pilot to production
Agent capabilities
Every step, every source, handled for you.
Describe the workflow and the output you need. Agents run each step, validate the result, and deliver straight into your systems.
Goal and success
- Business objective
- Workflow trigger
- Required output
- Completion criteria
- Turnaround requirement
- Failure and stop conditions
Data and context
- Approved web sources
- Documents and knowledge bases
- Databases and APIs
- Historical workflow data
- Data freshness requirements
- User and role permissions
Tools and actions
- Navigate and search
- Extract and classify
- Match and verify
- Retrieve and compare
- Update records
- Route, alert, or create tasks
Controls and evaluation
- Allowed actions
- Confidence thresholds
- Human approval points
- Escalation rules
- Source and action logs
- Accuracy, latency, and cost metrics
Need a custom agentic workflow?
Agents are custom-built around your process.
Built for autonomy
Agents that run your workflows, end to end.
Forage AI agents learn your workflow, execute it reliably at scale, and deliver output your team can use as-is.
Grounded
Context first, decisions second.
The information required for the task is collected and structured before decisions are made.
Role-based
A team, not a monolith.
Separate agents can navigate sources, extract information, resolve entities, retrieve evidence, validate outputs, and route the next action.
Governed
Every action inside the rails.
Business rules, output schemas, permissions, confidence thresholds, and stop conditions define how the workflow runs and which actions are permitted.
Judgment kept
Automation with judgment.
Routine, high-confidence cases can move through automatically. Ambiguous, sensitive, or high-impact cases are sent to the appropriate reviewer with the supporting context.
Plugged in
Lands in the systems you own.
Agents can be fitted into existing data platforms, cloud environments, databases, applications, and delivery workflows.
Measured
Production, measured.
Task completion, field accuracy, exception rate, latency, manual-review volume, and cost per completed task can be monitored throughout production.
AI agentrunning
▸gather context for the task
→pull approved websites, documents, APIs
→structure everything before deciding
✓context ready
▸assign the run to the team
→navigator maps · extractor captures
→resolver matches · validator checks
✓every role has a measurable result
▸load the rules of the run
→apply schemas, permissions, thresholds
→stop conditions armed
✓actions stay inside the rails
▸route by confidence
→routine cases flow through
⚑sensitive case sent to expert review
✓context attached for the reviewer
▸deliver into your stack
→warehouse, applications, delivery flows
→fitted to your existing environments
✓landed in the systems you own
▸report the run
→completion, accuracy, exceptions
→latency and cost tracked live
✓production, measured
The stack
Works with your stack.
Agents slot into your existing stack. Model-agnostic by design.
GPT-4o
Claude
Gemini
Llama 3
Mistral
PostgreSQL
Snowflake
BigQuery
Python
Node.js
Airflow
Kubernetes
Docker
Terraform
REST & gRPC
Datadog
Grafana
PagerDuty
Prometheus
Sentry
AWS
GCP
Azure
On-prem
LangGraph
LangChain
LlamaIndex
AutoGen
CrewAI
Claude
Gemini
Llama 3
Mistral
PostgreSQL
Snowflake
BigQuery
Python
Node.js
Airflow
Kubernetes
Docker
Terraform
REST & gRPC
Datadog
Grafana
PagerDuty
Prometheus
Sentry
AWS
GCP
Azure
On-prem
LangGraph
LangChain
LlamaIndex
AutoGen
CrewAI
Why Forage AI
The data layer and agent workflow, managed by one team.
Web data capability. Source discovery, website navigation, extraction, monitoring, and source maintenance can operate within the same agent workflow.
Document data capability. Agents can classify and extract information from reports, contracts, filings, forms, tables, images, and other document types before applying workflow rules.
Entity matching built in. Company, person, product, supplier, and other records can be reconciled across sources before the workflow updates a system or produces an output.
Model-agnostic design. Models, frameworks, tools, and retrieval methods are selected according to the workflow's reasoning, accuracy, latency, security, and cost requirements.
QA inside the workflow. Automated validation, confidence scoring, exception handling, and expert review are applied at the points where errors would affect the final outcome.
Managed from pilot through production. Forage AI handles scoping, architecture, integration, calibration, deployment, monitoring, and ongoing workflow maintenance.
Use cases
Put AI agents to work on data-heavy operations.
Build agents that collect from the web, read documents, reconcile records, apply business rules, and take approved actions across your systems.
Document operations agents
Classify incoming documents, extract fields and tables, compare related files, apply business rules, identify discrepancies, and route exceptions for review.
Entity resolution and enrichment agents
Match fragmented company, person, product, or supplier records across systems. Verify uncertain cases using internal and external evidence, then update the master record with confidence information.
Monitoring and response agents
Monitor websites, documents, databases, or system events for defined changes. Determine whether the change meets the workflow rules, then trigger an alert, record update, or review task.
Resources
All resources →
Latest and hottest in agentic AI.
Researching your way through AI agents? Start here.
Strong candidates are repeatable, multi-step workflows with changing inputs, unstructured information, measurable outcomes, and clear escalation rules.
Examples include external research, document review, entity matching, data enrichment, source monitoring, and exception routing. During scoping, Forage AI maps the existing process, data sources, systems, edge cases, and success criteria before recommending an agent design.
Yes. An agent workflow can connect approved websites, documents, knowledge bases, databases, APIs, cloud storage, and business applications.
Forage AI uses a model-agnostic approach. Models, tools, and frameworks are selected or integrated according to the required accuracy, latency, security, deployment environment, and cost. The deployment plan maps the agent into the existing technology stack.
The workflow can combine approved-source retrieval, deterministic business rules, structured output schemas, field validation, confidence thresholds, restricted tool permissions, stop conditions, and human approval.
Representative cases are tested against agreed acceptance criteria before production. Logs can record the sources used, actions taken, outputs produced, and exceptions raised.
Access is limited to the approved systems, sources, tools, users, and actions required for the workflow.
Credential handling, role-based permissions, data retention, deployment environment, action logging, and approval requirements are defined during solution design. Sensitive or high-impact actions can be configured to require human authorization before execution.
The process starts with one measurable workflow. The current accuracy, completion time, manual-review effort, exception rate, and operating cost are established as the baseline.
A pilot is then built using representative inputs and agreed acceptance criteria. Performance is measured through task-completion rate, field accuracy, exceptions, latency, and cost per completed task. Once the required thresholds are met, the workflow can expand across more sources, cases, users, or business units.
Forage AI manages integration, deployment, monitoring, evaluation, and ongoing updates as the workflow evolves.
Start foraging
The first call is to understand your workflow.
Tell us about your business and the process you want to automate.
- Workflow audit on your actual process
- A tailored agent plan scoped to your use case
- Pilot run on your data before you commit
Get in touch