Private AI systems

Generic AI was trained on everyone. Yours should be built for you.

Namakan engineers private AI systems around your contracts, policies, historical decisions, terminology, and approved language, then turns that intelligence into retrieval, workflows, and agents that can do real work inside your business.

Private data and policies

Fine-tuned reasoning layer

Retrieval, workflows, and agents

Fine-tuned models

Teach the model how your business actually thinks.

A fine-tuned model should not just "know documents." It should learn how your company classifies incidents, escalates risk, frames replies, and handles edge cases before retrieval or agents ever run.

Generic AIPublic model

Northforge Components has a field failure on forged valve assemblies used in washdown food-processing lines. The customer wants replacement parts shipped today, but the lot was produced on an older die set and the account is still inside the sanitation warranty window. Draft the response and tell ops what to do next.

Product issues like this usually require reviewing warranty terms, production history, and customer service policies before determining next steps.

No company-specific policy context

No citations or approved language

Unsafe for operational use

NamakanFine-tuned model

Draft the customer reply and internal handoff for the Northforge washdown-line valve failure.

Classify this as a sanitation-critical field issue. Pull the lot trace from the pre-May die set, open a same-day containment ticket, and release replacement assemblies only from the post-rework inventory tagged for washdown environments. In the customer reply, acknowledge the line-down risk, confirm expedited replacement under the sanitation warranty, and tell them quality is reviewing whether the older die-set lot needs broader recall action. Keep the tone direct, operational, and accountability-first, which is how Northforge handles plant-floor incidents.

Learns Northforge's incident-handling logic

Applies the company's plant-floor tone automatically

Knows when to trigger QA and containment workflows

Why this comes first

Train the intelligence layer before you automate the workflow.

A fine-tuned model does more than search. It learns how your business classifies incidents, escalates risk, frames replies, and handles edge cases before retrieval or agents ever run.

Training inputs

Contracts, SOPs, ticket history, CRM notes, playbooks, taxonomies, and approved responses.

Built after tuning

RAG systems, workflow automation, and AI employees running on a model that actually knows the business.

RAG pipeline

Private retrieval before the answer is written

For Northforge, retrieval means searching warranty rules, lot traces, and QA procedures before anyone answers a field-failure question.

✓washdown_warranty_policy_v3.pdf

✓forged_valve_lot_trace_may.csv

✓qa_containment_playbook.docx

✓field_failures_q2_2026.xlsx

Retrieved from 847 documents

Which lots are eligible for same-day replacement under the washdown warranty?

Based on the warranty policy, lot-trace file, and field-failure history: only the post-rework lots tagged for washdown environments qualify for immediate replacement, while pre-May die-set inventory requires QA hold and containment review before release.

Vector match: 0.94Chunks retrieved: 12Sources verified: 3

Agentic workflow

A workflow that can do the task, not just describe it

Here the workflow is built around a manufacturing incident: triage the failure, validate the lot, draft the response, and move ops forward without losing control of risk.

Current step

Research

Review the Northforge field-failure report, identify the affected forged valve assembly, and confirm whether the failure came from a washdown-line deployment.

Customer incident details collected from service intake

Affected assembly and plant environment verified

Initial failure context prepared for lot-trace review

AI employee

A task-running system with throughput and accountability

The AI employee example is also manufacturing-specific: processing field failures, preparing replacement comms, and handing only the highest-risk issues to the team.

Northforge Service Ops AIActive

17 incident tasks today5.8 hours saved

08:42 AM — Reviewed three new field failure tickets from distributor accounts

08:47 AM — Matched two incidents to the pre-May die-set lot trace

08:51 AM — Drafted plant-floor replacement updates for customer service

08:56 AM — Escalated one washdown-line case to QA and operations

Four systems we build after the model foundation is right

Fine-Tuned Models

AI trained on YOUR data

Generic AI doesn't know your business. We train AI models on your contracts, policies, and knowledge. It learns your voice. Your rules. Your domain.

✓Trained on your documents

✓Knows your brand voice

✓Cites your sources

✓40-60% accuracy improvement

Starting at $10K per project

RAG Pipelines

AI that knows YOUR documents

Your AI searches your entire knowledge base before answering. Contracts, CRM, emails, databases — all indexed and accessible in seconds.

✓Searches 100s of documents instantly

✓Always cites the source

✓Real-time data, not training data

✓No hallucinations on your data

Starting at $5K + $500/mo

Agentic Workflows

AI that actually DOES the work

Multi-step tasks completed autonomously. Research leads, enrich data, draft emails, update CRM — all without human intervention.

✓Multi-step task automation

✓Integrates with your tools

✓0 human approvals needed

✓Completes in seconds

Starting at $5K per workflow

Custom AI Employees

A full-time worker that never sleeps

AI that actually does the work. Researches leads, drafts emails, updates your CRM, schedules follow-ups. It's an employee that works 24/7.

✓Handles repetitive tasks