Agents that run your enterprise workflows. Not just demo them.
Multilingual platforms, voice AI agents, integrated with the enterprise applications you already run, with governance and compliance designed in. Deployed on Azure, AWS, GCP, or your existing stack.
Most agentic AI projects stall in the same three places.
We have shipped enough agentic systems to see where they break. Three patterns repeat across every engagement, whether the team is running a six-week pilot or scaling a third-generation platform across global branches.
Context starvation in production
Agents that pass a demo lose track in production because the agent harness has no memory architecture, no context routing, and no guardrails when the window runs out. Pilots stall here. We design the harness as a deliberate layer from week one: memory, retries, escalation, observability, and an eval harness that catches regressions before they ship.
Integration debt
A working agent that cannot reach the CRM, the ERP, the document store, or the rest of the enterprise applications you already run is a clean demo, not a production system. Most teams underestimate integration time by 3x. We scope it as the first track of work, not an afterthought.
Governance and compliance gaps
Enterprise buyers will not sign off on agents that cannot show audit trails, role-based access, rollback paths, deterministic guardrails, and a human-in-the-loop (HITL) path when the agent gets something wrong. We design governance into the architecture from week one, not bolted on at the end.
If your last pilot stalled in one of these three places, the playbook to get past it is what we do.
Production agentic systems, designed for enterprise scale.
Every agentic system we ship is designed around a deliberate agent harness, with governance and integrations as first-class layers, not afterthoughts. Four capability tracks that show up in every engagement, drawn from production work across finance, healthcare, and global enterprise platforms.
Multilingual document intelligence
Extract, classify, and route complex multi-format documents at scale. Used inside enterprise platforms processing thousands of documents a month with 95%+ automated extraction. Multilingual support across global branches.
Enterprise application integration
Bidirectional, idempotent integrations with your CRM, ERP, document stores, telephony, and the rest of the enterprise applications you already run. MCP-compatible connectors where your stack supports it, custom adapters where it does not. Agents that read and write back across your systems of record, not chatbots that hand off to email.
Voice AI agents
Production voice agents for outbound and inbound conversations, integrated with HubSpot and other CRMs. Three voice agents in production today across sales prospecting, recruitment screening, and customer support.
Agent orchestration with a production harness
Multi-agent systems wrapped in a deliberately designed production harness: audit trails, role-based access, rollback paths, deterministic guardrails, human-in-the-loop (HITL), and an eval harness that catches regressions before any change ships. Built on agent orchestration platforms, deployed on Azure, AWS, GCP, or your existing stack.
Two tracks. Same engineering team. Different scale of commitment.
Most teams come to us with one of two questions. "We have validated the use case and need an execution partner," or "We need to prove this internally before we commit budget." We support both paths, with the same engineering team behind either one.
For teams past the demo phase
Multi-quarter engagement with a dedicated team.
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1Weeks 1 to 3
Discovery and architecture
Your data, integrations, and governance constraints mapped. We design the agent architecture before we write a line of code.
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2Weeks 4 to 8
First agent in production
One capability live in your environment, integrated with at least one system of record. Measured, instrumented, ready to expand.
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3Quarter 2 onwards
Scale and governance hardening
Agent fleet, audit trails, role-based access, rollback paths. Production telemetry that lets your team override the agent when it gets something wrong.
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4Ongoing
Embedded engineering
Dedicated team that knows your domain. No handoff to a different team after launch.
For teams still building the internal case
Four to six weeks. Working agent, written assessment.
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1Week 1
Use-case assessment
We map your data, your bottlenecks, and which use case will move first. Output: a written scope.
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2Week 2
Design
Agent architecture, integration plan, governance scope. Reviewed with your engineering and compliance leads before build starts.
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3Weeks 3 to 5
MVP build
Working agent, integrated with at least one system of record. Real data, not a sandbox.
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4Week 6
Measured outcome and written assessment
What worked, what to scale, what to skip. A document you can take to your CFO or board.
The discovery work is the engagement. Every conversation starts with a written reply from engineering, not a pre-sales discovery call. The data gathering, the architecture sketch, the scope document. That is where the engagement begins.
We do not sell you our platform. We build inside yours. The agent harness, the integrations, the governance, all yours after launch, deployed on your cloud, with your IP. No vendor lock-in, no licensing on agents you commissioned us to build.
Agentic systems we have built, scaled, and put into production
Talk to an Engineering Lead
Tell us what you are working on. A real engineering lead replies within one business day with a written assessment, a case study, or the scope answer you came for, whichever fits your situation.
You can come to us with:
- An agentic AI use case you are scoping
- A pilot that stalled and you need to get past
- A production platform that needs agentic capabilities added
- A multilingual or document-heavy workflow you want automated
- A voice AI workflow you want in production
- An enterprise application integration (CRM, ERP, telephony, document store) that needs an agent inside it
You will speak with engineering, not sales. First reply comes from someone who can answer technical questions about agent architecture, integrations, and governance. Not someone scheduling another discovery call.
Requesting a specific case study? Mention it in the message field.
Send a Message
A few details so the right engineer follows up.
Routed to the right engineer
We read what you sent and route it to someone who actually works on agentic systems, not to sales.
A written reply within one business day
You get a substantive response, not a calendar link. If the situation is clear, we send architecture notes or a scope sketch directly.
A call only if it helps
No discovery-call merry-go-round. We schedule a conversation only when there is something specific to discuss.
Nine questions we hear before every engagement.
Honest answers. If your question is not here, send it via the form above and an engineer will reply.
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Yes. The Pilot Path exists for teams who need to prove the idea internally before committing budget. If your team has already validated the use case, has executive sponsorship, and is ready to fund a multi-quarter engagement, we start with discovery and architecture, not with a pilot.
Most of our long-running production engagements skipped the pilot path entirely.
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Yes. Agentic AI patterns repeat across regulated and customer-facing industries. The architecture stays consistent. The integrations and the governance shape adapt.
- Finance: multilingual document intelligence and investment opportunity discovery for US enterprise clients
- Wellness and hospitality: customer engagement agent integrated with CRM and telephony
- Operations: voice AI in production across sales, recruitment, and customer support
- SaaS product: agentic capabilities added to a long-running enterprise platform we have engineered since 2018
If your industry is not on this list, the engineering pattern is usually the same. The first call covers what is different in your domain.
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Governance is designed into the agent harness from week one, not bolted on at the end. Every production agent ships with:
- Audit trails for every agent action, with reasoning logged so your team can see what the agent decided and why
- Role-based access control aligned to your existing identity stack
- Deterministic guardrails on what the agent can and cannot do, alongside model-judged safety checks
- Rollback paths and a human-in-the-loop (HITL) mechanism
- Telemetry that surfaces drift, low-confidence decisions, and escalation points
- An eval harness that catches regressions before any agent change ships to production
What happens when the agent gets something wrong: the HITL path pulls a person into the loop, the action is reversed if reversible, and we tune the agent's reasoning or guardrails based on what the audit trail shows. The agent does not silently fail and it does not keep making the same wrong call.
For regulated industries (financial services, healthcare), we work with your compliance lead during architecture, not after launch. Our flagship multilingual enterprise platform passed enterprise governance review before going live across global branches.
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Your data stays in your environment. We do not train any models on your data.
- Pilot runs inside your cloud (Azure, AWS, or GCP) using your existing identity, observability, and security tooling
- For higher-sensitivity engagements, we set up private inference (self-hosted models or private cloud endpoints) so data does not leave your perimeter
- Mutual NDAs are signed before scoping starts
- All data flows are auditable from week one, including which agent action touched which record
If your team has a specific data residency, retention, or DPA requirement, the architecture is shaped around it before any code is written.
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Yes. Bidirectional, idempotent integrations with your CRM, ERP, document stores, telephony, and the rest of the enterprise applications you already run are a first-track work item on every engagement, not an afterthought. We use MCP-compatible connectors where your stack supports them, and custom adapters where it does not. Every agent write is idempotent so retries do not duplicate records.
Most teams underestimate integration time by 3x. We scope it up front so the agent is reading and writing real enterprise data from week one of production, not handing off to email.
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MCP (Model Context Protocol) is an emerging open standard for how AI agents connect to enterprise tools, data sources, and APIs. Think of it as a structured contract between the agent and the systems it needs to use, replacing the bespoke integration code that used to sit between every agent and every tool.
Why it matters for your integration:
- One agent can talk to many systems through a common interface, instead of one custom integration per system
- The contract itself is auditable, which makes governance and compliance cleaner
- When your stack changes (new CRM, new ERP), the agent does not need to be re-engineered, only re-pointed
- It future-proofs your agentic AI investment against the model-of-the-quarter problem
Our position: where your stack already supports MCP, we use it. Where it does not, we build custom adapters that wrap your existing systems so the agent talks to all of them through the same interface. Either way, the integration layer is designed to be portable, not locked to a particular framework or vendor.
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Two shapes:
- Pilot Path: 4 to 6 weeks. Week 1 use-case assessment, week 2 design, weeks 3 to 5 MVP build, week 6 measured outcome and written assessment.
- Production Build: 8 weeks to first agent in production, with scale and governance hardening in quarter 2 onwards. Engagements typically continue for multiple quarters as the agent fleet expands.
We provide a written scope before the engagement starts. No surprise timeline padding mid-project.
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Yes. We are cloud-agnostic and work with whichever stack you already have. Most of our production agentic systems run on Azure-based infrastructure with the client's existing identity, observability, and security tooling. We have shipped on AWS and GCP as well.
A stack migration is not a requirement for engaging with us.
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Both, in production today.
The multilingual enterprise platform we have engineered through its third generation processes complex multi-format documents across the client's global branches, with 95%+ automated extraction.
Voice AI is running in production across three of our engagements, integrated with HubSpot and other CRMs, handling outbound prospecting, recruitment screening, and customer support.
Where agentic AI fits in your stack.
If you came here looking for agentic AI but the actual work is adjacent, these are the pages worth your time. Pick the one that matches your real problem.
AI-Driven Application Modernization
For teams whose first step is not a new agentic system, but adding agentic capabilities to a platform they already run. Modernize the codebase, then layer agent orchestration inside the existing flow without disrupting production.
See how we modernize with AI EngineeringAI-Assisted Development
Every AnAr engineer ships with AI assistance built into the workflow. If you want your team or our team to build faster, with better code review and faster iteration cycles, this is how we do it across every engagement.
See how AI assists the engineering ProductProduct Engineering
For teams building a product from scratch or scaling an existing one. Dedicated engineering inside your stack, multi-quarter engagements, with agentic capabilities layered in as the product matures.
See how we engineer products