AI Agents That
Retire Legacy Systems.
When a box product cannot be made to fit, we build the agent that does - production-ready, observable, and safe to put in front of customers. Bring your aging CRM, ERP, or ticketing tool; we will give you the agent that replaces it.
Six Capabilities for Production-Grade Agents
Not demos. Not POCs. Agents that run in production, with the evals, observability, and guardrails it takes to keep them there.
Legacy System Modernization
Replace aging CRMs, ERPs, ticketing tools, and home-grown apps with agents that work against the same underlying data - without the box-product tax.
- Read-only mode first - agent works alongside the legacy system
- Gradual cut-over - shift one workflow at a time, not a big bang
- Data preservation - migrate or proxy, never lose history
- Run-rate cost reductions - kill the legacy license at the right moment
Custom Agent Development
Domain-specific agents built on your data, your rules, your workflows. Production-grade evals, guardrails, and observability from day one.
- Eval suite with golden test cases, scoring rubrics
- Prompt and model version control
- Cost and latency monitoring
- Human-in-the-loop for high-stakes actions
RPA & Workflow Replacement
AI-native automation beyond brittle screen-scraping bots. Reasoning over context, not just clicking buttons in a fixed sequence.
- Replace fragile UI bots with API-driven agents
- Handle exceptions the legacy bot could not
- Decisioning based on context, not just rules
- Audit trail of every action and decision
LLM & Tool Integration
Function calling, MCP, and RAG over your knowledge base. Plug agents into Salesforce, HubSpot, Dynamics, Slack, Jira, and your data warehouse.
- Model Context Protocol (MCP) for clean tool boundaries
- Function calling with strict schemas
- RAG with vector search + reranking
- API gateways and rate-limit-aware retries
Multi-Agent Orchestration
Specialist agents that coordinate to ship work end-to-end - planner, executor, reviewer, verifier - with audit trails at every step.
- Planner / executor / verifier patterns
- Stateful conversation across handoffs
- Token and cost budgets per workflow
- Replayable runs for incident review
Trust, Safety & Governance
PII redaction, prompt-injection defenses, role-based tool access, and full conversation logs for compliance review.
- PII detection and redaction in prompts and outputs
- Prompt-injection and jailbreak defenses
- Role-based access on the tool layer
- Conversation log retention with redaction policy
From Prompt to Production-Grade Agent
A four-phase delivery model designed for the LLM era - tight loops, quantitative evals, and shadow-mode rollouts.
Discover
Understand the workflow being replaced, the data the agent needs access to, and the failure modes that cannot happen in production.
Prototype
Build the agent against a representative slice of your data. Get to a working demo in 2-3 weeks - not 3 months.
Eval
Golden test cases, scoring rubrics, side-by-side comparisons. Quantitative pass/fail before going anywhere near production.
Deploy
Production rollout in shadow mode, then read-only, then write access on a graduated basis. Continuous monitoring after go-live.
Three Scenarios Custom Agents Solve
Anonymized results from recent custom AI agent engagements across industries.
Retiring a 15-year-old custom CRM
Challenge
In-house CRM built 15 years ago. Original developers long gone. UI hated by users. Adding any new field takes 3 weeks of dev work. Vendor support nonexistent.
Result
Agent built on top of the existing database (read mode first), surfacing the data through chat plus a lightweight modern UI. Six months later, the legacy UI was decommissioned. Users got their time back, IT got the maintenance bill back.
AI underwriting assistant
Challenge
Insurance underwriters spending 60% of their time gathering and cross-referencing data from policy docs, credit reports, and internal systems before they could even start a decision.
Result
Custom agent that ingests application packets, queries internal systems, summarizes credit reports, flags policy exceptions, and produces a structured underwriting recommendation. Underwriter approves or overrides. 4.5x faster cycle time, full audit trail.
End-to-end RFP response automation
Challenge
Sales engineering team drowning in RFPs. 3-5 day response time, missing easy questions, copying answers from old responses that no longer match product reality.
Result
Planner agent breaks RFP into question batches, executor agents draft answers using a continuously updated knowledge base, reviewer agent cross-checks claims against product docs, human approves. Response time down from days to hours.
Why Teams Choose Us Over an AI Demo Shop
Evals-First Engineering
Every agent ships with an eval suite from day one. We can quantitatively compare versions before rolling forward - no vibes-based decisions.
Grounded in Your Data
Agents answer from your knowledge base, not the model weights. Retrieval architectures designed and tuned per workload - no copy-paste RAG.
Safety as a Default
PII redaction, prompt-injection defenses, RBAC on tool access, and human-in-the-loop for high-stakes actions. We do not ship agents that can do damage they should not.
Production-Ready, Not POC
We have shipped agents to production - cost monitoring, version control, replayable runs, on-call. Most demos do not survive contact with reality. Ours do.
AI Agent FAQs
Packaged AI (Agentforce, Breeze, Copilot Studio) wins when your work mostly lives inside that platform - e.g., a service agent that operates on Salesforce cases, or a Copilot that lives in Teams. A custom agent wins when (1) the work spans systems that no single platform owns, (2) you need to retire an aging system rather than extend it, or (3) you need control over the underlying LLM, evals, and observability. We will tell you which path fits in the discovery call.
Anthropic Claude, OpenAI GPT, Google Gemini, AWS Bedrock-hosted models (Claude, Llama, Titan), and open-weight models when self-hosting is required. We pick based on the workload - reasoning depth, tool use quality, latency, cost, and where your data must live.
Every agent ships with an eval suite from day one - golden test cases, scoring rubrics, regression detection. Production runs are logged and sampled for ongoing eval. When the agent fails, we know about it before the customer does, and we can compare versions against the same test set before rolling forward.
Three layers: (1) Retrieval grounding - agents answer from your data, not the model weights. (2) Guardrails for PII, prompt injection, and out-of-scope requests. (3) Human-in-the-loop for high-stakes actions. We do not deploy agents that take destructive actions without confirmation.
Yes. Through function calling and MCP, agents can update records in Salesforce / HubSpot / Dynamics, create tickets in Jira, send approved messages in Slack, run queries against your data warehouse, or trigger workflows in your existing systems. Tool access is RBAC-scoped so the agent can only do what its role allows.
Custom agents need different ops than packaged tools - LLM versions change, prompts drift, business needs evolve. Our managed agent ops covers eval regression detection, prompt and model version management, cost monitoring, and quarterly capability reviews. Sized to your usage, not a fixed retainer.
What Legacy System Are You Replacing?
30-minute call. Bring the workflow you wish you could retire, and we will tell you whether a custom agent is the right answer - and what it would take to ship one.
Book AI Agent Discovery Call