AI Agent Development · Austin

AI agent development in Austin.

Austin runs on B2B SaaS, fintech, and dev tools. The agent workloads here look different from coastal cities: less regulated back office, more in-product workflow automation, trust-and-safety triage at marketplace scale, and CS or RevOps agents glued to a Salesforce, HubSpot, Snowflake, and Looker stack that every company in town seems to share.

Indeed runs resume parsing and interview scheduling at job-board volume. Bumble runs report triage across millions of users. Q2 Holdings runs KYC for community banks. WP Engine runs tiered customer support across thousands of WordPress shops. Dell Technologies and AMD run procurement and engineering ticket triage at enterprise scale. Every one of these is an agent that lives inside an existing SaaS or data platform, not a standalone product.

Fixed-scope projects. Discovery first, fixed quote after, four to eight weeks of build with weekly demos on Central time.

How we work

Tell us about the workflow.

The shape of agent work in Austin.

The first pattern is in-product workflow automation. Indeed handles tens of millions of resume submissions a month through the Apply pipeline, and the workflow around each application (parsing, deduplication, ATS push, screening question routing, interview scheduling) is mostly deterministic with a long tail of unstructured inputs. An agent that owns the parsing and the scheduling layer reduces engineer time on the long tail without replacing the existing pipeline.

The second pattern is trust-and-safety triage. Bumble runs report-handling queues at consumer-platform scale. The mix of report types (harassment, fake profile, spam, payment fraud, policy violation) maps cleanly to an agent classifier with a policy taxonomy. The moderator approves the agent's proposed action. Auto-action only fires for known-bad patterns with a sampling audit, because wrongful suspension on a dating or jobs platform carries real cost.

The third pattern is fintech KYC and compliance ops. Q2 Holdings serves hundreds of community banks. Charles Schwab runs Austin operations for advisor and retirement platforms. Both have KYC, AML, and CIP workflows that combine sanctions screening, document review, beneficial-ownership lookup, and adverse-media checks. The compliance analyst is the bottleneck and the regulatory signer. The agent handles the assembly.

The fourth pattern is enterprise procurement and engineering ticket triage. Dell Technologies in Round Rock and AMD on Burnet Road both run procurement and engineering helpdesk queues at enterprise scale. The agent classifies the ticket, pulls the requester's history and entitlement, drafts the response or PR comment, and routes to the right approver. The approver sees the full reasoning trace, not a black-box ticket bot.

The fifth pattern is RevOps and growth automation across Salesforce, HubSpot, and Snowflake. The Austin SaaS stack is relatively standardized: Salesforce or HubSpot as the CRM, Snowflake as the warehouse, dbt as the transform layer, Looker or Hex as the BI tool, Hightouch or Census as the reverse-ETL. Agents that fit inside that stack (lead enrichment, account health classification, expansion-opportunity surfacing) hit production faster because the data plumbing already exists.

Every one of these is an agent candidate where the integration surface area is well-understood and the unit economics work at Austin SaaS gross margins. The hard problem is not the model. The hard problem is the eval set and the human gate design.

What we build for Austin teams.

Six engineering components show up in nearly every agent we ship for SaaS, fintech, and dev-tools teams in the city.

Agent loop inside your SaaS stack

LangGraph or OpenAI Assistants depending on the workflow shape. The agent reads and writes through your existing dbt models, Salesforce objects, and HubSpot properties so the audit trail lives in the systems your team already operates.

Tool integrations for the Austin stack

Salesforce, HubSpot, Snowflake, dbt, Looker, Hex, Hightouch, Census, Persona, Alloy, Stripe, Segment, Zendesk, Intercom. Typed schemas, retry policies, and idempotency keys on every write.

Policy and taxonomy classifiers

Trust-and-safety classification, support intent routing, KYC risk scoring. Built against a labeled set you provide. Versioned, evaluated, and gated by CI before any change reaches production traffic.

Human-in-the-loop gates

Any irreversible action (suspend a user, deny a KYC case, send a customer-facing message, push a deal to closed-won) routes to a human in Slack, Teams, or your existing review queue with the full reasoning trace.

Observability through Datadog or Honeycomb

OpenTelemetry spans for every LLM and tool call. Model version, token counts, latency, cost, trajectory length, override flag. Searchable by account, ticket, or run ID. Cost dashboards by feature.

Production eval harness

Braintrust or Promptfoo against a labeled set, gating CI. Task completion rate, hallucination rate, tool-call accuracy. Regression suite runs on every prompt or model change so a quiet drift does not reach customers.

Why this work fits Austin.

Austin is the second-largest B2B SaaS hub in the country after the Bay Area, with the added bonus of a thick fintech and enterprise-hardware base. Dell Technologies, Oracle's Austin campus, Indeed, Bumble, Q2 Holdings, WP Engine, AMD, Charles Schwab, Procore, and a long tail of late-stage SaaS companies along the Domain corridor all run engineering and ops organizations big enough to justify in-house agent work, but most of them have not built it yet because the eval and gate design is the hard part, not the model selection.

The local stack converges. Salesforce or HubSpot, Snowflake, dbt, Looker or Hex, Hightouch or Census, Zendesk or Intercom, Segment. The integration patterns are well-trodden, which means the project timeline is dominated by the agent design and the eval harness, not by data plumbing. That is why fixed-scope agent work lands well here.

We work remotely with Austin clients on Central time. Discovery calls fit between 9am and 6pm CT, demos happen weekly, and the codebase ships to a repository you own. The team overlaps with your business hours by design, and we ship to your environment, not a managed cloud you have to migrate to later.

Industries where we see strongest fit: B2B SaaS customer ops, fintech KYC and compliance, marketplace trust-and-safety, enterprise procurement, dev-tools support automation, and growth and RevOps automation across the standard Austin data stack.

Questions from Austin clients.

How does an agent fit into a Salesforce and HubSpot stack that already has Flow and Workflow automations?: Most Austin SaaS companies already have a layer of Salesforce Flow, HubSpot workflows, and Zapier glue that handles deterministic routing. An agent is the right tool one layer up, where the decision needs reasoning over unstructured input (a free-text support ticket, a sales discovery note, an account-health signal) before the deterministic automation can take over. We treat the agent as the input cleaner and the decider, and we keep the existing Flow and Workflow automations as the executor. The agent writes the structured fields, the existing automation routes from there. This pattern protects the audit trail and the change history your RevOps team already trusts.
Can an agent run KYC and onboarding workflows at a Q2 Holdings or Charles Schwab scale?: Yes, for the document and data gathering portion of the flow. KYC at a fintech that serves community banks (Q2's customer base) or at a wealth manager combines OFAC and sanctions screening, address verification, beneficial-ownership lookup, document review (passport, utility bill, formation documents), and adverse-media screening. The agent owns the orchestration: it calls Persona or Alloy for identity verification, queries OFAC and the SEC EDGAR feed, parses the formation documents with a document parser, and assembles the case file. A compliance analyst reviews the assembled package and signs the decision. The BSA/AML record-keeping requirements are met because the agent logs every data source, every model version, and the analyst override. We do not let the agent close out a high-risk case without a human gate.
What does trust-and-safety triage look like for a Bumble or Indeed-scale platform?: Trust-and-safety queues at marketplace scale share a structure: a heterogeneous mix of reports come in (harassment, fake profiles, spam, CSAM, payment fraud, policy violations), and a small team of moderators has to triage to a policy-appropriate action within an SLA. An agent classifies the report against your policy taxonomy, pulls the relevant account history and signal data, and proposes an action (warning, suspension, ban, escalate to legal, escalate to NCMEC). The moderator approves or overrides with one click. The agent never auto-bans without a moderator gate on platforms where wrongful action carries reputational cost. For known-bad signal combinations (e.g., automated bot patterns), we can configure the agent to auto-action with a sampling audit. The classifier eval set is the work product the moderation lead reviews monthly.
How do you integrate an agent into the Austin SaaS stack of Snowflake plus Looker plus dbt?: Three integration patterns we use often. First, the agent reads from Snowflake through a typed query layer (dbt-defined models, never raw SQL into the warehouse) so it can pull account-health, usage, or pipeline data with a stable schema. Second, the agent writes back to Snowflake or to a Postgres state table that feeds dbt and Looker, so its decisions and rationale show up in the same BI tooling your RevOps and growth teams already use. Third, for closed-loop workflows (e.g., an agent that classifies a churn-risk account and triggers a CSM outreach), we wire the agent into Hightouch or Census to push the segment into Salesforce and HubSpot rather than directly hitting the CRM API. This keeps the activation layer in the system your data team operates.

Scope an agent for your Austin team.

Describe the workflow, the system of record, and the volume. We'll reply within one business day with a rough scope, a recommended architecture, and a price range. No commitment, no sales call required.

AI agent development in Austin.

Fixed-scope projects. Discovery first, fixed quote after, four to eight weeks of build with weekly demos on Central time.

The shape of agent work in Austin.

What we build for Austin teams.

Six engineering components show up in nearly every agent we ship for SaaS, fintech, and dev-tools teams in the city.

Agent loop inside your SaaS stack

Tool integrations for the Austin stack

Salesforce, HubSpot, Snowflake, dbt, Looker, Hex, Hightouch, Census, Persona, Alloy, Stripe, Segment, Zendesk, Intercom. Typed schemas, retry policies, and idempotency keys on every write.

Policy and taxonomy classifiers

Trust-and-safety classification, support intent routing, KYC risk scoring. Built against a labeled set you provide. Versioned, evaluated, and gated by CI before any change reaches production traffic.

Human-in-the-loop gates

Observability through Datadog or Honeycomb

OpenTelemetry spans for every LLM and tool call. Model version, token counts, latency, cost, trajectory length, override flag. Searchable by account, ticket, or run ID. Cost dashboards by feature.

Production eval harness

Why this work fits Austin.

Questions from Austin clients.

How does an agent fit into a Salesforce and HubSpot stack that already has Flow and Workflow automations?

Most Austin SaaS companies already have a layer of Salesforce Flow, HubSpot workflows, and Zapier glue that handles deterministic routing. An agent is the right tool one layer up, where the decision needs reasoning over unstructured input (a free-text support ticket, a sales discovery note, an account-health signal) before the deterministic automation can take over. We treat the agent as the input cleaner and the decider, and we keep the existing Flow and Workflow automations as the executor. The agent writes the structured fields, the existing automation routes from there. This pattern protects the audit trail and the change history your RevOps team already trusts.

Can an agent run KYC and onboarding workflows at a Q2 Holdings or Charles Schwab scale?

Yes, for the document and data gathering portion of the flow. KYC at a fintech that serves community banks (Q2's customer base) or at a wealth manager combines OFAC and sanctions screening, address verification, beneficial-ownership lookup, document review (passport, utility bill, formation documents), and adverse-media screening. The agent owns the orchestration: it calls Persona or Alloy for identity verification, queries OFAC and the SEC EDGAR feed, parses the formation documents with a document parser, and assembles the case file. A compliance analyst reviews the assembled package and signs the decision. The BSA/AML record-keeping requirements are met because the agent logs every data source, every model version, and the analyst override. We do not let the agent close out a high-risk case without a human gate.

What does trust-and-safety triage look like for a Bumble or Indeed-scale platform?

Trust-and-safety queues at marketplace scale share a structure: a heterogeneous mix of reports come in (harassment, fake profiles, spam, CSAM, payment fraud, policy violations), and a small team of moderators has to triage to a policy-appropriate action within an SLA. An agent classifies the report against your policy taxonomy, pulls the relevant account history and signal data, and proposes an action (warning, suspension, ban, escalate to legal, escalate to NCMEC). The moderator approves or overrides with one click. The agent never auto-bans without a moderator gate on platforms where wrongful action carries reputational cost. For known-bad signal combinations (e.g., automated bot patterns), we can configure the agent to auto-action with a sampling audit. The classifier eval set is the work product the moderation lead reviews monthly.

How do you integrate an agent into the Austin SaaS stack of Snowflake plus Looker plus dbt?

Three integration patterns we use often. First, the agent reads from Snowflake through a typed query layer (dbt-defined models, never raw SQL into the warehouse) so it can pull account-health, usage, or pipeline data with a stable schema. Second, the agent writes back to Snowflake or to a Postgres state table that feeds dbt and Looker, so its decisions and rationale show up in the same BI tooling your RevOps and growth teams already use. Third, for closed-loop workflows (e.g., an agent that classifies a churn-risk account and triggers a CSM outreach), we wire the agent into Hightouch or Census to push the segment into Salesforce and HubSpot rather than directly hitting the CRM API. This keeps the activation layer in the system your data team operates.

Scope an agent for your Austin team.