From legacy integrations to production-ready multi-agent systems, we design and deploy custom AI agents that handle real business workflows end to end.
Workflow type
Complex, proprietary, or exception-heavy processes
Standard workflows with predictable inputs and outputs
Integration depth
Deep connectors to ERP, CRM, legacy systems, proprietary databases
Standard integrations only — Salesforce, HubSpot, common SaaS
Compliance posture
Regulated or sensitive data that must stay in your environment
Non-sensitive data with no compliance constraints
Workflow logic
Proprietary prompt chains, fine-tuning, or sensitive process logic
No customization required beyond configuration
Agent architecture
Non-standard reasoning patterns or custom orchestration layers
Bounded by what the platform supports out of the box
Time to first value
Longer — design, build, test cycles add 8–20 weeks
Faster — configure and launch in days or weeks
Long-term ownership
Full ownership of architecture, models, security, and future direction
Dependent on vendor roadmap, pricing changes, and feature limits
We map your workflows, identify automation gaps, define measurable KPIs, and recommend an agent architecture before any code is written. Clients who skip this step typically rebuild in two months.
Full-cycle development of production agents: LLM selection, tool integration, memory design, and guardrails built to your specific workflows — not adapted from a template.
Retrieval-augmented agents grounded in your proprietary data, documents, databases, APIs. Outputs are auditable and traceable to the source, which matters when regulators ask questions.
Most AI agent projects stall because discovery is rushed and architecture decisions are made before requirements are clear. This process is designed to prevent that.
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Stakeholder interviews, process audits, and data inventories identify the highest-ROI automation opportunities. We define measurable success criteria at this stage — not after build.
We select LLMs, agent frameworks (LangGraph, CrewAI, AutoGen), and orchestration patterns matched to your specific use case. This decision shapes everything downstream.
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We audit data sources, build ingestion pipelines, configure vector stores, and implement retrieval strategies. Agents are only as accurate as the data they can access.
Execute repeatable, rule-bound workflows autonomously, like data entry, document processing, and compliance checks. Clients typically see a 60–80% reduction in manual effort on these workflows within the first quarter.
Handle customer inquiries, triage support tickets, and resolve issues across channels with context retention and escalation logic. Built for production load, not demo environments.
Retrieve, synthesize, and summarize data across multiple sources — market intelligence, competitor analysis, internal reporting. Useful anywhere analysts spend time pulling and formatting data manually.
Ingest structured and unstructured data to generate risk scores, recommendations, and action lists. Common use cases: loan underwriting, claims adjudication, supplier risk.
Code review agents, CI/CD assistants, incident response tools, and internal knowledge retrieval systems. Engineering teams that use these typically report meaningful gains in review cycle speed.
Planner, executor, critic, and retrieval agents working in concert. These handle complex, cross-functional workflows that adapt in real time — the architecture that powers our most demanding enterprise deployments.
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Validating a single use case before committing to the budget
1 agent, 1 workflow, limited integration
Starting from $15K
Single use-case rollout with full enterprise requirements
1 agent, full integration, guardrails, monitoring
Starting from $60K
Enterprise-wide agent programs across departments
Orchestrated multi-agent system, multi-dept rollout
Starting from $200K
Four structures matched to different stages of an AI agent program.
Locked scope, timeline, and cost. Works well for PoCs and well-defined single-agent builds where requirements are stable from day one. No billing surprises.
A 4–8 person embedded team working within your org for 6+ months on multi-agent programs. Clients use this when they need capacity and accountability without hiring.
Post-deployment monitoring, tuning, eval runs, and iteration. For enterprises requiring ongoing performance guarantees without building internal AI headcount.
Development and delivery processes are ISO 27001 and SOC 2 Type II certified, providing auditable information security management across every engagement.
Architects with direct experience in data residency, consent management, and breach notification requirements under each framework. Not theoretical familiarity — we've worked through real incidents.
Automated PII detection and redaction pipelines mask sensitive data before it reaches LLM inference layers. This is implemented at the pipeline level, not as a post-processing step.
Fine-grained RBAC policies govern which users, systems, and agents can access knowledge bases or trigger actions — aligned to your existing IAM infrastructure.
Output validation layers, topic restriction, toxicity filtering, and structured parsing prevent harmful or policy-violating agent responses from reaching users or downstream systems.
High-stakes or low-confidence decisions are automatically routed to human reviewers with full context and audit trail. Throughput is preserved; accountability is maintained.
Agents with access to structured and unstructured knowledge bases — answering questions, summarizing documents, surfacing data on demand.
Agents that take actions: sending emails, updating CRM records, filing tickets, querying APIs. Autonomous within defined guardrails with full logging of every action taken.
Agents managing multi-step processes end-to-end, intake, processing, decision, action, notification, with conditional logic and exception handling.
Coordinated systems where specialized agents collaborate, delegate, and escalate across complex cross-functional workflows. We've shipped these in production for insurance claims, clinical documentation, and logistics ops.
We connect agents to ERP, CRM, HRIS, and legacy systems via APIs and custom connectors. No rip-and-replace required. Clients are typically surprised how much existing infrastructure we can work with.
Post-deployment observability, eval pipelines, drift detection, and continuous tuning. Agents degrade if you don't manage them — this is the service that prevents that.
Agents are built with defined tools, memory structures, prompt chains, and integration layers connecting to your APIs, databases, and enterprise systems.
Output validation, PII redaction, role-based access, content filtering, and human-in-the-loop checkpoints are built in — not reviewed at the end.
Structured eval frameworks, red-team testing, task completion measurement, and hallucination rate tracking before any production rollout.
Deploy to cloud, hybrid, or on-prem; monitoring dashboards, alert pipelines, and full documentation. Optional retainer support for ongoing performance management.
Post-deployment, agents don't stay static. We implement feedback loops that capture user corrections, failed task logs, and edge case outputs.
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A focused upfront engagement to assess use cases, define requirements, validate feasibility, and produce an implementation roadmap.
Coordinated systems with planner, shared memory, dynamic delegation, and cross-agent error recovery. Handles enterprise-wide workflows at scale.
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+1 408 365-4638
contact@folio3.ai
6701 Koll Center Parkway, #250 Pleasanton, CA 94566
