Wolverine Solution builds startup AI agents—RAG, tool workflows, evals—on fixed scope. Pricing, timeline, stack, FAQ, and next steps for US/EU founders.
If you’re hiring an ai agent development company for startups, you’re not shopping for a demo chatbot. You need an agent that calls real tools against Postgres, Stripe, HubSpot, or NetSuite, fails closed when retrieval is wrong, and ships under a fixed SOW before runway runs out. Wolverine Solution (Montréal; US and EU delivery) builds AI & LLM systems for seed-stage SaaS founders and regional wholesale distributors: RAG pipelines, LangGraph or OpenAI Agents SDK workflows, human-in-the-loop gates, and pytest eval harnesses in GitHub Actions—priced and dated in the contract, not billed as open-ended discovery.
An AI agent development company for startups designs, builds, and deploys software agents that perceive data, decide, and act on your behalf—with named tools, approval gates for writes, and measurable acceptance tests. You get a version-controlled repo and a deploy runbook. Not a black-box vendor portal. Not an endless retainer.
Startups need more than retrieval-only Q&A. A useful v1 agent usually includes:
If the sales deck opens with “autonomous agents handle 70% of tasks” but never names tool contracts or eval thresholds, you’re buying a pitch—not a product.
[Internal link: what is an LLM evaluation harness for startups]
For a single-workflow production agent v1—2–6 tools, HITL on writes, optional RAG, eval harness, staging and prod—plan roughly $40,000–$85,000 on a fixed-scope SOW when APIs are documented and the workflow already happens manually today. Multi-tool agents with custom evals and deep ERP integrations can reach $80,000–$120,000; we quote ranges upfront so you can align spend with your next funding milestone.
What moves the number:
| Factor | Lower end | Higher end |
|---|---|---|
| Write tools + HITL payloads | 1–2 gated writes | 5+ writes, custom approval UX |
| Data source | Clean Postgres / REST APIs | Messy Notion, PDF SOPs, NetSuite logs |
| Auth / tenancy | Clerk or Auth0 already live | Greenfield multi-tenant rules |
| RAG scope | None or small corpus | Large hybrid index + re-ranking |
| Product leadership | Founder freezes scope | Embedded product strategy hours in SOW |
We lock KPIs in the contract—e.g., golden-set pass rate ≥ agreed threshold, latency ≤300 ms on p95 tool routes—so acceptance is testable, not subjective. A shop that won’t publish a “not building” list doesn’t have fixed scope. It has a burn rate.
A focused MVP ships in 8–12 weeks from kickoff to production when requirements and APIs are ready. Complex multi-system agents may run 16–20 weeks, always with milestone sign-offs before the next phase starts.
Typical 8–12 week breakdown:
Parallel capacity—product judgment, HITL UX, RAG, and deploy in one calendar window—is why founders hire an agency instead of stacking a freelancer behind a demo that never gets eval gates.
Fixed-scope delivery gives seed teams a defined budget, deadline, and single accountable partner—milestones and acceptance tests in the SOW, not time-and-materials until “the agent feels smart.” That predictability matters for regional distributors and early-stage SaaS founders who need to show ROI to investors quickly.
Compared with broad MVP shops (Shipkit, Brocoders) or product studios (Sophylabs, Very Creatives, DBB Software), a specialist agent build adds:
We sit under AI & LLM Systems alongside fine-tuning only when it beats retrieval, plus DevOps & Cloud (Terraform, AWS Lambda, GCP Cloud Run) and embedded Product Strategy when founders can’t freeze scope alone.
[Internal link: fixed-scope SaaS MVP contract clauses for technical founders]
Production agents need typed tool contracts, interruptible orchestration, and infra your team can debug at 2 a.m.—not an opaque “agent platform” you can’t extend after handoff.
Wolverine Solution’s typical stack:
LangGraph is a means, not a requirement. The non-negotiables are interruptible tool calls, typed schemas, and tests your founding engineer inherits.
[Internal link: RAG pipeline development for SaaS knowledge bases]
Every stage is scoped, priced, and signed off before the next begins. No surprise hourly bills mid-build.
Start with repetitive, data-heavy work that still needs judgment—invoice validation, inventory reorder triggers, lead qualification, support triage. If the job is “given X context, propose Y action” and you have historical examples of good and bad outcomes, it’s a strong candidate. Skip agents when Zapier, a CRUD admin, or retrieval-only Q&A already closes the loop with less blast radius. No golden examples means no eval set—and no defensible ship gate.
No. Most seed teams use API access to OpenAI, Anthropic, or hosted open weights; fine-tuning is optional and only when it beats retrieval on your acceptance tests. Wolverine Solution handles model selection, cost optimization, and fallback routing so you don’t need an in-house ML team. What you should own is the tool contract, eval harness, and deployment account—not necessarily the weights.
Agents run in VPC-isolated environments with IAM least-privilege, encryption at rest (KMS) and in transit (TLS), and role-based access on approval UIs. Tool payloads are logged with PII redaction; write tools require HITL or hard blocks. For US and EU customers, pick model API and datastore regions up front (AWS us-east-1 / eu-west-1 or GCP equivalents) and document data flows in the SOW—not just marketing copy about compliance.
Yes. We deliver modular, version-controlled code—new tools are added via schema + eval cases + Terraform updates, not a ground-up redesign. That’s why v1 should be one narrow workflow with measurable error rates rather than a general assistant that half-does every department. Extension work is scoped as change orders with updated golden sets.
Track golden-set pass rate in CI, false tool-call rate in production, median time-to-human-decision on gated writes, and cost per successful run. A sane launch bar: CI golden-set pass ≥ contract threshold, false writes under 1–2% of attempts, and a weekly review of top failure traces. Revisit those KPIs 90 days post-launch before funding a second workflow.
Ready to scope an agent that won’t freestyle in production? Wolverine Solution builds fixed-scope AI & LLM systems—agentic workflows, RAG pipelines, and eval harnesses—for early-stage SaaS founders and ops-heavy SMBs across the US and EU. Bring the workflow you already run manually (tools, roles, “never auto” list). We return a one-page SOW: tools, gates, acceptance tests, and what we’re not building—before any code starts. Book a scoping call.