How OutreachSpark embedded AI agents into its product without building an agent engine

Founder-led, eight people, shipping fast. OutreachSpark needed to put AI agent intelligence behind its outreach workflows, but building an orchestration engine, prompt routing, billing and audit trails would have taken months. Instead, they wired Orkeste's Agent-as-an-API behind their UI and shipped in days.

Industry: B2B SaaS
Team size: 8 people
Use case: Embedded AI agents via API

70%
Reduction in manual outreach time per campaign
~12k
API calls per month to Orkeste agents
3 days
From signed contract to first onboarded customer

The challenge

OutreachSpark helps B2B sales teams run personalised multichannel campaigns. By late 2025, customers were asking for the same thing in different words: "can the product write better personalisations on its own?", "can it route my leads based on intent signals?", "can it follow up without me writing the sequence?".

DevlakAi, the founder, looked at the build-versus-buy decision honestly. Putting AI agents inside the product meant building, at minimum: a prompt orchestration layer, a fallback chain across LLM providers, a credit metering system, an audit trail, a human-in-the-loop UI, and a way to expose all of that securely to the customer's account. Every one of those is a six-month engineering project on its own.

The team is eight people. Spending nine months building agent infrastructure instead of shipping product features was not an option.

The solution

OutreachSpark adopted Orkeste's Agent-as-an-API. Instead of building agents inside the product, they design them inside Orkeste, with prompts, knowledge sources and connectors, then call them from their backend with a single HTTP request.

Two agents went live in the first sprint:

A lead enrichment agent that takes a company domain and returns structured firmographics, recent news and a confidence score.
A personalisation agent that drafts a tailored opening line based on the lead's details and the campaign's value proposition.

The implementation

Wiring the agents into the product took two engineers about three days end-to-end. The integration pattern is simple: when a customer launches a campaign, the OutreachSpark backend hits the Orkeste endpoint for each lead, gets back a structured response, and stores it in their existing campaign table.

Every call returns the credits consumed, so OutreachSpark can pass usage costs through to its own pricing transparently. The audit trail of every agent run is available in the Orkeste dashboard, which their CS team uses when a customer asks why a specific draft was generated.

The results

Three months in, the metrics OutreachSpark tracked tell the story. Manual outreach prep time per campaign dropped by 70%. The team handles roughly twelve thousand agent calls per month, all metered transparently through the credit system. New customers go from signed contract to first live campaign in under three days, because the AI features that used to require manual setup now ship pre-configured with the product.

On the engineering side, the win is even clearer. Zero lines of code maintain prompt orchestration, model fallbacks or audit logging. Every improvement to the underlying agents, better prompts, new connectors, faster models, ships through Orkeste without an OutreachSpark deploy.

"We're an eight-person team. Building an agent engine would have killed our roadmap for a year. With Orkeste, we ship AI features at the speed our customers ask for them, and we keep our engineering focus on the product, not on LLM plumbing."

DevlakAi

Founder, OutreachSpark

Disclosure: OutreachSpark is operated by the same founder as Orkeste. We chose to dogfood our own product because we believe builders should ship on the infrastructure they trust their own bets on. The metrics above are real and measured.

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