Fitting agentic AI into the enterprise stack.
In the contact center, deploying agentic AI is an architecture decision as much as a product one, which models, whose cloud, where the data lives, and how it wires into telephony, CRM and ITSM. Our delivery team makes those calls with you and builds the implementation to match.
Before you deploy, what do you actually have to decide?
BPOs and large enterprises rarely stall on the model, they stall on the architecture around it. So the real questions aren't about features. They're the ones worth answering before a single agent goes live:
One model, or the right model per task?
The most sophisticated deployments don't standardize on a single LLM. A routing layer sends each task to the best-fit model, which optimizes quality and cost and avoids single-provider lock-in, model deprecation, price changes and capability gaps stop being your problem.
Frontier API, or open-weight in your VPC?
Calling a frontier API sends prompts off your network; a DPA or BAA reduces but doesn't remove the exposure. Workloads touching PII, PHI or financial records often run open-weight models (e.g. Llama) in a private VPC or air-gapped, self-hosting can cut inference cost 70–90% at scale, past a ~10–30M-tokens/day crossover, at the price of ~2–3 MLOps engineers.
Bedrock, Azure OpenAI or Vertex?
All three contractually exclude your prompts from training. FedRAMP High narrows it fast for regulated work (Bedrock and Azure OpenAI hold it; Vertex not yet GA). Azure suits Microsoft-anchored estates; Vertex suits GCP and custom-model work; Bedrock spans the broadest model set. We deploy into whichever you already run.
Buy the platform, or build your own?
Most enterprises buy the platform and build the last-mile customization. Agents layer onto your existing dialer or CCaaS without a full migration, and wire into CRM and ITSM over MCP. Compliance baselines, SOC 2 Type II, HIPAA with BAA, PII redaction, should be in the base product, not a paid tier.
What does a production architecture actually look like?
Strip away the detail and it's three moving parts, where do your conversations come from, what reasons over them, and what does it act on? The interesting question is where the model and the data sit.
Your channels
Voice, chat, email, SMS & WhatsApp, on your telephony or ours.
auto-AGENTS™ Engine
Reason, act & self-improve, over MCP.
Your systems
CRM, ITSM, telephony & knowledge, connected, not replaced.
Where does your data need to live?
Same platform, three topologies. The answer depends less on us and more on your residency, latency and control requirements, so where do yours land?
SaaS
Multi-tenant in our cloud, frontier models via API. Live in weeks with the least operational lift.
Private cloud / VPC
Inside your AWS, Azure or GCP account, Bedrock, Azure OpenAI or Vertex. Data stays in your tenant.
On-prem · air-gapped
Open-weight models self-hosted in your data center. Nothing ever leaves your boundary.
Open-weight or closed-weight, which, and when?
The single biggest model question we help you answer. Rarely one or the other, so where should each earn its place? Here's the rule of thumb.
- You want peak reasoning on general traffic
- Speed to production matters most
- Volumes are moderate and variable
- A DPA / BAA covers your data path
- You handle PII, PHI or financial data
- Data must stay in your VPC or on-prem
- Volume is high & steady (~10–30M+ tokens/day)
- You have the MLOps capacity to run it
auto-AGENTS™ are model-agnostic and route each turn to the right one, with TokenTrim™ cutting tokens ~10× either way. We help you draw the line and wire the router.
A pod that has shipped this before.
Solution architects, integration and MLOps engineers who deploy agentic AI in enterprise contact centers for a living.
A repeatable path from kickoff to hypercare.
Every engagement follows the same four phases, with clear owners, exit criteria and a readiness checkpoint at each boundary.
Discover & scope
Map the use case, systems and policies; agree success metrics, data flows and the integration surface. Output: a signed delivery plan and architecture.
Integrate & ingest
Wire telephony and CRM/ITSM over MCP, ingest and ground knowledge, stand up sandboxes with redaction and tenant-scoped access.
Build & evaluate
Configure agents, run the CQB adversarial eval suite, harden guardrails, and complete the security and compliance review.
Launch & hypercare
Readiness sign-off, staged cutover with fallback, then 30 days of live monitoring, tuning and knowledge transfer to your team.
However your systems are built, we make it fit.
Implementation is where our solutions meet the big scheme of your company's systems. Our team handles the customization, connection and custom engineering so auto-AGENTS™ run the way you need, in the environment you require.
Customer implementation
A dedicated delivery pod stands auto-AGENTS™ up end to end, scoped to your use cases, systems and teams, live in weeks not quarters.
White-gloveCustomization & configuration
Tailored agents, workflows, prompts, routing and guardrails, configured to your policies, brand voice and processes.
TailoredBring your own LLM
Prefer your own models? We connect auto-AGENTS™ to your GPT, Gemini, Claude or self-hosted Llama endpoints, model-agnostic by design.
BYO modelsOn-prem & private cloud
Deploy in our cloud, your VPC, a private cloud, on-prem, or pinned to a region, to meet your data-residency and security requirements.
Cloud · VPC · on-premCustom integrations & glue
Beyond our 170+ connectors, we build the custom MCP tools, APIs and glue code to wire auto-AGENTS™ into your bespoke and legacy systems.
Custom engineeringData & knowledge onboarding
Ingest and structure your knowledge base, policies and historical data, plus migration from legacy bots and contact-center platforms.
Ingestion · MigrationSecurity & compliance setup
SSO/SAML, field-level encryption, tenant-scoped KMS, PII redaction and audit, configured to your controls and reviewed with your team.
Enterprise-gradeCustom agent development
Net-new agents and specialized workflows built for your business, with adversarial evals gating every build before it ships.
Eval-gatedGo-live, hypercare & managed ops
A staged go-live runbook, a 30-day hypercare window, and optional managed operations so we run it with you.
Launch · RunContact-center modernization
Modernize without ripping out the call center: agents layer in front of your CCaaS over connectors, even traversing legacy IVRs that have no API, so you phase from deflection to assist to autonomy on your timeline.
Deflect → assist → autonomousSomething more bespoke?
Tell us your environment and constraints, we’ll scope the implementation live in your demo.
Talk to us →Launch faster, with less risk on your team.
Time to launch
A dedicated pod runs workstreams in parallel against a shared plan, so most teams reach a production go-live in four to six weeks.
Eval-gated launches
No build reaches a live caller until the CQB suite passes, intent, latency, hallucination and policy gates all green.
Hypercare included
Thirty days of post-launch monitoring and tuning, with a documented handoff so your team owns the agent with confidence.
Implementation, answered.
Most engagements reach a production go-live in four to six weeks. A dedicated delivery pod runs the integration, knowledge, build and eval workstreams in parallel against a shared plan, with weekly readiness checkpoints so launch is a non-event rather than a deadline scramble.
Genesys, Amazon Connect and Twilio are first-class, with SIP and additional CCaaS platforms supported on request. We bind them to the agent runtime and route turns with sub-500ms p95 latency, barge-in and natural turn-taking intact.
We connect your systems, Salesforce, ServiceNow, Zendesk, Dynamics and internal tools, over the Model Context Protocol. With 170+ connectors the agent reads and writes inside your policies during and after the interaction, every action scoped, audited and reversible.
Every build is graded against the CQB adversarial eval suite, intent accuracy, inference latency, hallucination rate and policy adherence, and a single readiness review confirms integrations, security and the runbook are all green. We cut over in stages with fallback in place.
For 30 days after launch the pod monitors live traffic, tunes the agent against real interactions, resolves edge cases fast, and transfers knowledge to your team, leaving you with a documented, self-improving system you fully own.
Bring your hardest use case. We'll ship it.
In a 30-minute working session we'll scope the integration, map your systems, and lay out the path to a live, eval-gated agent.