Service · 04 / 06 · Intelligence

AI that does real work. Not demos.

Chatbots, workflow tools, internal knowledge assistants, support automation, document processing and reporting automation. We build for the workflow your team runs Tuesday morning — not the keynote stage.

First release
2–5 wks
Models
Claude · GPT · open
Human-in-loop
By default
Hosting
Your infra
How it runs

AI proposes. Humans approve. Work gets done.

Every workflow we ship has the same shape — input, model step, confidence gate, human review when stakes are real, action. Visible, traceable, debuggable.

01 · Input

Trigger

  • New support ticket
  • Webhook · email · doc upload
02 · AI

Model step

  • Claude / GPT / open
  • Classify · summarise · draft
03 · Gate

Confidence check

  • ≥ 0.82 → auto
  • < 0.82 → human review
04 · Action

Outcome

  • Reply sent · ticket routed
  • All steps logged

Every step is logged: input, prompt version, model, output, latency, cost. Reproducible answers, debuggable failures.

Claude Sonnet 4.6GPT-4oLlama 3.1MistralOpen-weights
Capabilities

Six places AI earns its keep.

CB

AI chatbots

Customer-facing or internal — trained on your docs, your tone, your product. With escalation rules that respect users.

  • RAG over your docs
  • Tone-of-voice tuning
  • Hand-off rules
  • Multi-language
WF

Workflow automation

AI inside the workflow, not bolted onto it. Triage, summarise, classify, route — humans approve consequential calls.

  • Triage / routing
  • Summarisation
  • Classification
  • Approval gates
KA

Knowledge assistants

RAG over docs, runbooks, Slack and tickets. Your new hire's first colleague is the org's institutional memory.

  • Doc + Slack + tickets
  • Source citations
  • Permission-aware
  • Continuous reindex
SA

Support automation

Suggested replies in your agent UI, auto-classification, and clean hand-off when confidence drops below threshold.

  • Suggested replies
  • Auto-tagging
  • Confidence gating
  • Feedback loop
DP

Document processing

Extract structured data from invoices, contracts, forms or PDFs. With confidence scores and review queues.

  • OCR + LLM extract
  • Schema-validated output
  • Review queue
  • Audit log
RA

Reporting automation

Weekly business summaries, anomaly alerts and natural-language Q&A over your real data — not stitched dashboards.

  • Weekly briefs
  • Anomaly alerts
  • NL → SQL
  • Trust scores
Trust by default

The bits that make this safe to ship.

AI that ships to real users needs more than a clever prompt. These are the four properties we build in from day one — not after the first incident.

Privacy

Your data, your infrastructure

We deploy to your cloud or ours-on-your-account. No model provider sees what they shouldn't.

Evals

We run them. We share them.

Every release ships with a real eval suite — accuracy, hallucination rate, refusal rate — and you see the numbers.

Human-in-loop

Default for consequential calls

AI proposes. Humans approve when stakes are real. Confidence gates are explicit, not hidden.

Auditability

Every decision, traceable

Input, prompt version, model, output, latency, cost — all logged. You can reconstruct any answer.

Already running an AI feature that’s drifting? We also do focused eval-and-harden engagements — bring us a system, we’ll bring you a real failure-mode report and a plan to fix it. Start there.

FAQ

Questions, answered.

If something isn’t covered here, write to hello@corefluxsolutions.com — you’ll get a real answer within a working day.

Which models do you use?
Model-agnostic by default — we pick what fits the workload. Claude (Sonnet / Opus / Haiku), GPT family, Gemini, and open-weights (Llama, Mistral, Qwen) when privacy or cost demands it. We don’t lock you in to one provider.
Will this hallucinate and embarrass us?
Honest answer: any unguarded LLM can. That’s why we ship with evals, confidence gating, source citations and human hand-off on ambiguity. We measure the failure modes and report them, not hide them.
Do you train on our data?
We don’t train base models. We do build retrieval indexes and fine-tunes that live in your environment, on your data. Nothing leaves your perimeter without explicit configuration.
How long to a first usable release?
2–5 weeks for a focused use case (a single workflow, a single assistant). Production hardening with evals, monitoring and human-in-loop adds another 2–3 weeks.
What does it cost to run?
Inference cost depends on traffic and model choice. We design for cost from day one — routing cheap models for triage, expensive models only when needed. Monthly cost is part of the blueprint, not a surprise.
Can we own the system without you?
Yes. Source code, prompts, eval suite, infra — all in your name. Optional retainer if you’d rather we keep an eye on drift and regressions.
Let's build

Have a system in mind? Let's sketch it together.

Drop your email and a line about the problem. We'll reply within one working day.

Or write directly to hello@corefluxsolutions.com — we read everything.