Tool-calling and MCP integrations
Agents that safely operate your CRMs, ERPs, ticketing systems and internal APIs — with scoped, revocable permissions.
Featured Solution
Intelligent, Goal-Driven AI Agents
We design agents that plan, use tools, call APIs, and complete multi-step work safely inside your enterprise environment.
- 6–10 wksfrom discovery to production pilot
- 70%of tier-1 tasks handled end-to-end
- 100%of agent actions audited
Capabilities
Everything a production agent needs
Build production AI agents that automate complex enterprise tasks with tools, memory, and guardrails.
Memory and context management
Short-term working memory, long-term knowledge and retrieval pipelines so agents stay accurate across long tasks.
Human-in-the-loop approvals
Approval gates on sensitive actions, clear escalation paths, and interfaces your teams actually want to use.
Observability and eval gates
Every run traced, scored and regression-tested — so quality is measured, not assumed.
How it works
From a prompt to finished work — safely
We engineer the full loop: reasoning, tool use, verification and escalation. Not a chatbot with API access — a system you can put in front of real workflows.
Act, not just answer
Agents that operate your actual systems
We connect agents to the tools where work happens — Salesforce, SAP, ServiceNow, Jira, internal APIs — through hardened, permissions-aware integrations built on MCP and native APIs.
- Scoped tool permissions per agent and per user role
- Structured outputs validated before any write action
- Full audit trail of every tool call and its result
SalesforceSlackSAP
JiraSSnowflake
ServiceNowTeamsDrive
OAuthSSOPermissions-awareReal-time sync
Enterprise context
Grounded in your knowledge, scoped to your permissions
Agents retrieve from your documents, tickets and databases with the caller's access rights enforced at query time — so answers and actions never leak across permission boundaries.
- Retrieval pipelines tuned for your data, not generic RAG
- Long-running task memory with explicit state management
- Citations on every answer that informs a decision
DocsCRMWiki
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Answer with citationsscoped to the user's permissions [1] [2]
Trust and control
Guardrails and human approval where it matters
High-impact actions pause for human review. Policies on data access, spend and scope are enforced in the runtime — not left to the prompt.
- Approval workflows for sensitive or irreversible actions
- Policy engine blocks out-of-scope tool calls at runtime
- Eval gates in CI so regressions never reach production
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Use cases
Where enterprises deploy AI agents first
Customer support triage
Classify, enrich and resolve tier-1 tickets; escalate edge cases with full context attached.
Back-office operations
Invoice matching, claims intake, order exceptions — multi-step work across ERP and email.
Research and analysis
Agents that gather, verify and synthesize market, legal or competitive intelligence into briefs.
Sales operations
CRM hygiene, meeting prep, follow-up drafting and pipeline summaries from live data.
IT service desk
Password resets, access requests and diagnostics resolved end-to-end with approval gates.
Compliance workflows
Evidence collection, control checks and audit-ready documentation on a schedule.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
The difference was engineering discipline. Our agent doesn't just demo well — it survives month three, because evals, guardrails and observability were built in from the first sprint.
Works with your stack
Your models, your clouds, your systems
We build on the frontier and open-source models that fit each task, deploy in your cloud, and integrate with the systems your teams already run.
- Anthropic Claude
- OpenAI
- Gemini
- Llama
- AWS Bedrock
- Azure AI
- Vertex AI
- LangGraph
- MCP
- Salesforce
- SAP
- ServiceNow
FAQ
Common questions
How is this different from a chatbot or copilot?
A chatbot answers questions. An agent completes work: it plans multi-step tasks, calls tools, checks its own output and escalates when unsure. We engineer the surrounding system — permissions, evals, observability — that makes that safe in production.
How do you keep agents from taking wrong actions?
Three layers: scoped tool permissions so agents can only touch what they should, runtime policy checks that block out-of-scope calls, and human approval gates on sensitive or irreversible actions. Everything is logged and traceable.
What does a typical first engagement look like?
We start with one high-value workflow, ship a production pilot in six to ten weeks with evals and monitoring included, then scale to adjacent workflows once the value is proven.
Which models and frameworks do you use?
We are model-agnostic. We select per task across Claude, GPT, Gemini and open-source models, and route between them for cost and quality. Orchestration is typically LangGraph or a custom runtime, with MCP for tool connectivity.
Can agents run inside our VPC?
Yes. We deploy into private VPCs, restricted networks and on-prem environments, with your existing IAM, secrets management and logging stack.
Next step
Put an agent on your hardest workflow
Tell us the process that eats your team's time — we'll show you what an agent can safely take over.