Custom AI Models

Generic AI tools are impressive in demos, but most businesses need something more specific: models and agents that understand your domain, connect to your systems, and produce outputs you can trust.

Enterflow builds custom AI models and AI-powered workflows—from retrieval-augmented generation (RAG) to multi-step agents—designed around your data, processes, and compliance requirements.

What “Custom AI Models” means in practice

We deliver AI that is usable in production—not just prompts.

Typical outcomes include:

• Domain-aware assistants for internal teams (support, ops, finance, legal)

• Automated document/email workflows (triage, routing, drafting, extraction, validation)

• Search and knowledge systems that answer questions grounded in your sources (RAG)

• Classification and enrichment (tagging, prioritization, entity extraction, normalization)

• Decision support with explainable outputs and confidence signals

• AI agents that execute multi-step tasks across your tools (with guardrails)

For non-technical stakeholders: this is AI that reduces manual work, shortens cycle times, and standardizes quality.
For technical stakeholders: this is a production-grade system with observability, evaluation, and controllable risk.

Where it delivers ROI fastest (use cases)

If your team spends time searching, summarizing, replying, routing, or reconciling information, custom AI typically pays off quickly in:

• Customer support & success: faster resolution, better triage, consistent answers

• Sales & rev ops: lead qualification, account research, proposal and email drafting

• Operations: SOP guidance, incident summaries, workflow automation

• Finance: invoice/PO support, anomaly detection, narrative reporting

• Legal & compliance: contract Q&A, policy checks, evidence packet summaries

• Internal knowledge: “Ask your company” search across docs, tickets, wikis, CRM

How we build it (clear enough for non-technical teams)

Our approach is designed to produce reliable outcomes and reduce risk:

1. Use-case definition: success metrics, failure modes, scope boundaries

2. Data mapping: where truth lives (docs, CRM, ticketing, ERP, databases)

3. Solution architecture: select patterns (RAG, tool-using agent, classifier, fine-tune)

4. Guardrails & policy: privacy, access control, allowed actions, escalation paths

5. Evaluation & QA: test sets, acceptance thresholds, regression checks

6. Integration & rollout: API, UI, Slack/Teams, browser extensions, or embedded workflows

7. Monitoring & iteration: production telemetry and continuous improvement

Tech stack (for technical stakeholders)

We implement modern LLM application infrastructure, typically including:

LLM orchestration & agent tooling

• LangChain for chains, tools, agents, and workflow orchestration

• LangSmith for tracing, debugging, evaluation, and monitoring in production

• Optional: graph-based orchestration (e.g., agent graphs) when workflows require state and branching

Retrieval-Augmented Generation (RAG)

• Embeddings + vector search with chunking strategies tuned for your content

• Vector stores such as pgvector (Postgres), Pinecone, Weaviate, or OpenSearch (based on your constraints)

• Hybrid retrieval (vector + keyword) when precision matters

• Re-ranking and citation/grounding patterns to reduce hallucinations

Models & deployment flexibility

• Support for leading model providers and open-source models depending on requirements

• Optional fine-tuning or domain adaptation when RAG alone is not sufficient

• Deployment options: cloud, VPC, or on-prem depending on data sensitivity

Quality, safety, and reliability

• Prompt/version management

• Automated evaluation harnesses (golden datasets, regression tests)

• Rate limiting, caching, and failover strategies

• Access control and audit logging (especially for tool-using agents)

Security and compliance by design

We support enterprise-grade safeguards, including:

• Data minimization and purpose limitation

• Role-based access and secret management

• Configurable retention and deletion policies

• Environment isolation (dev/staging/prod)

• Contractual processor commitments (DPA where applicable)

As a default posture: your data is used only to deliver your solution unless explicitly agreed otherwise.

The “key data” we track (so success is measurable)

We define and monitor metrics that matter to both leadership and engineering:

• Task success rate (did the AI complete the job correctly?)

• Human escalation rate (what percentage needs review?)

• Answer groundedness (supported by internal sources vs. unsupported claims)

• Latency (time-to-first-token and end-to-end task time)

• Cost per task (and savings vs. manual effort)

• User adoption (usage frequency, satisfaction feedback, deflection rates)

When to choose RAG vs. fine-tuning vs. agents

A practical rule of thumb:

• RAG when the “truth” lives in your documents/systems and must be cited

• Fine-tuning when you need consistent style/format or specialized behavior at scale

• Agents when the work requires multi-step actions across tools (with approvals and guardrails)

We often combine these patterns—for example, a RAG-backed agent that can search your knowledge base, draft a response, and create a ticket, while keeping humans in control.

Ready to build something that works in production?

If you share:

• your target workflow,

• 2–3 example inputs (docs, tickets, emails),

• and the systems you want to integrate (CRM, helpdesk, ERP, Slack/Teams),

we can propose an architecture, rollout plan, and measurable success criteria.

Contact: info@enterflow.ai
Website: https://enterflow.ai/