Service · 04 / 06 · Intelligence

AI that does real work. Not demos.

Q: 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.

Q: 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.

Q: 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.

Q: 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.

Q: 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.

Q: 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.

Chatbots, knowledge assistants, support and document automation — built for the workflow your team runs Tuesday morning, not the keynote stage.

Talk to an AI engineer↗See the workflow

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, action.

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.

Claude Sonnet 4.6GPT-4oLlama 3.1MistralOpen-weights

Capabilities

Six places AI earns its keep.

AI chatbots

Customer-facing or internal — trained on your docs, your tone, your product.

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

Workflow automation

Triage, summarise, classify, route — humans approve consequential calls.

Triage / routing
Summarisation
Classification
Approval gates

Knowledge assistants

RAG over docs, runbooks, Slack and tickets.

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

Support automation

Suggested replies, auto-classification and clean hand-off when confidence drops.

Suggested replies
Auto-tagging
Confidence gating
Feedback loop

Document processing

Extract structured data from invoices, contracts, forms or PDFs.

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

Reporting automation

Weekly business summaries, anomaly alerts and natural-language Q&A over your real data.

Weekly briefs
Anomaly alerts
NL → SQL
Trust scores

Trust by default

The bits that make this safe to ship.

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.

Evals

We run them. We share them.

Every release ships with a real eval suite, and you see the numbers.

Human-in-loop

Default for consequential calls

AI proposes. Humans approve when stakes are real.

Auditability

Every decision, traceable

Everything is logged. You can reconstruct any answer.

Already running an AI feature that’s drifting? We also do focused eval-and-harden engagements. Start there.

FAQ

Questions, answered.

If something isn’t covered here, write to Info@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.

Tell us a little about the problem. We'll reply within one working day.

Write directly to Info@corefluxsolutions.com

+1 (832) 338-7915 +1 (832) 226-3449