AI custom development, for the use cases off-the-shelf can't reach.
When the use case is too specific for an off-the-shelf AI tool, custom development is the answer. Multi-agent orchestration, fine-tuned models, custom evaluation pipelines, RAG systems on your proprietary data. Senior engineers, code in your repo, monitored in production.
Custom AI that actually held up in production.
Four real custom AI builds, four real outcomes.
Multi-agent triage system
Anchor's incoming requests flow through a 3-agent pipeline: classification → enrichment → resolution. Built end-to-end in 6 weeks.
RAG over 12-system patient data
Anchor's clinicians ask plain-language questions across 12 systems. Custom RAG pipeline retrieves and answers from the right source.
Custom eval pipeline + monitoring
Promptly's AI features ship with a custom evaluation pipeline: 500-example test set, automated scoring, drift detection.
Product catalog AI search
Hunter Pumps' product catalog has natural-language search built on a custom embedding pipeline tied to Unleashed inventory.
When custom AI development fits, and when it truly doesn't.
Below is the honest read.
Right fit when
- Off-the-shelf AI tools don't cover your use case or perform poorly on your data.
- Your use case justifies investment in evaluation infrastructure and ongoing monitoring.
- You need code committed to your repo and supportable by your team.
- Sensitive data requires custom infra (HIPAA, GDPR data residency, SOC 2 controls).
- You have or can collect the labeled data needed to evaluate or fine-tune.
Wrong fit when
- An off-the-shelf tool covers 80% and you're trying to bridge the last 20% with custom code.
- Your use case will change every quarter and a hardcoded custom pipeline will be obsolete in 6 months.
- Your team can't maintain custom AI code post-handoff and there's no retainer plan.
- You're chasing complexity for its own sake. We push back on this directly.
How custom AI actually runs in production.
Custom AI in production is more than a model. Below is the structure.
Pipeline + embeddings + retrieval
Data pipelines feeding the AI system. Embedding generation, vector store, retrieval logic. Re-ranking. Source-of-truth ground for grounded outputs.
LLM orchestration
Multi-step workflows. Tool use. Function calling. Agent coordination. Structured output validation. Confidence routing. Cost-optimized model selection per task.
Eval + monitoring + cost
Continuous eval pipeline. Quality monitoring. Drift detection. Cost tracking. Slack alerts. Production runbook for common failure modes.
From kickoff to custom AI in production.
Six steps. Built to ship custom AI that holds up under real-world load.
Discovery
Two sessions with stakeholders. Use case clarity, data shape, success metrics, eval criteria, infra constraints. Output: technical specification with measured-impact targets.
Eval
We build the evaluation pipeline before we build the model. Test set, scoring methodology, baseline measurement. Eval runs in CI on every change. No model ships without eval.
Build
Senior engineers architect the data pipeline, models, agent logic, and tool use. TypeScript or Python. Code in your repo. Tested against eval set throughout.
Deploy
Stage in non-prod. Shadow mode for 1 to 2 weeks. Compare against baseline. Production rollout in stages with feature flags. Monitoring wired before flag flip.
Monitor
Daily quality reports. Slack alerts. Cost monitoring. Drift detection. Hallucination detection. Production runbook documented.
Hand off
Code in your repo. Architecture documentation. Eval methodology. Operational runbook. Your team owns it. Optional retainer for ongoing tuning.
Inside a custom AI build.
Real deliverables, not bullet points. Below is the typical scope, fixed-fee from $48,000.
Spec + Eval
- ·Technical specification with measured-impact targets
- ·Evaluation pipeline with 100-500 example test set
- ·Architecture document covering data, models, infra
- ·Cost estimate at production volume
- ·Sign-off gate before build
Build
- ·Data pipeline (extraction, embedding, vector store)
- ·LLM orchestration (multi-step, tool use, agents)
- ·Confidence-based human-review routing
- ·Eval suite running in CI
- ·Code review against your team's standards
Deploy
- ·Shadow mode for 1 to 2 weeks
- ·Production rollout with feature flags
- ·Daily quality reports + Slack alerts
- ·Drift and hallucination detection wired
Hand off
- ·Code in your repo with documentation
- ·Architecture document (PDF + editable)
- ·Operational runbook
- ·Optimization roadmap for months 4-12
Per-system. Complexity-aware.
Light custom AI: $24,500 (single-step pipeline with eval). Standard: $48,000 (multi-step orchestration, RAG, evaluation infra). Enterprise: $98,000+ (multi-agent systems, fine-tuning, sustained monitoring infra).
Things people ask.
Do you fine-tune models?+
Sometimes. Fine-tuning is rarely the right answer for most use cases. RAG, prompt engineering, and structured output validation cover 90% of needs at lower cost. We fine-tune when the data justifies it and the use case requires it.
What about RAG over our internal documents?+
Yes. We've built RAG systems over knowledge bases, support transcripts, sales call recordings, product docs, and proprietary databases. Embedding pipeline, vector store, retrieval, re-ranking, and grounded generation are all part of the build.
Where does the AI run?+
Your infra. AWS Lambda or Bedrock, GCP Cloud Functions or Vertex AI, Azure OpenAI, Vercel Edge, or self-hosted on Kubernetes. We don't host AI on our infra (vendor-lock and data trust concerns).
How do you handle data privacy?+
HIPAA-aware setups with PHI controls. GDPR data residency in EU regions. SOC 2 controls. Self-hosted open-source models for strict data residency. Anthropic's enterprise plan with zero data retention for sensitive customers.
Can you support after launch?+
Yes via a maintenance retainer ($5K to $25K monthly depending on scope). Without a retainer, the build includes a 30-day post-launch warranty: we fix any bugs we shipped at no extra cost.
How do we get started?+
Book a 30-minute strategy call. We'll cover use case, data, infra, and the right approach. Proposal within 48 hours if we're a fit.
How INSIDEA works
