AI Workflow Teardown — Nexus Point Group

Executive summary

A solo management consulting practice currently running 1 active engagement and ~2 first-time discovery meetings per month has, at this scale, ~7.5 hours per month of recurring administrative and drafting work that should be automated. At your $250/hr effective rate, that's ~$1,865/month of time recoverable today. The same five automations scale linearly: at 4 active engagements and 6 discovery meetings per month, the recovery hits ~$5,000/month without rewriting any of the prompts. The recommendations are sequenced so the highest-ROI Easy item ships in week one and the system grows with the practice.

At a glance

* Includes second-order benefit (faster reply to hot leads lifts close rate). Numbers above use your current $250/hr effective rate, $2k average engagement value, and the cadence assumptions described in the next section.

ROI by opportunity (visual)

How we estimated

Hourly rate: $250/hr (your stated effective rate). Hours-per-week per workflow are estimated from your current cadence — 1 active engagement, 2 first-time discovery meetings per month, ~$2k average engagement value — not from generic benchmarks. Each opportunity below shows two numbers: (1) what you'd recover today, and (2) what you'd recover at 4 engagements / 6 meetings per month, so you can see the leverage curve. Automation feasibility is scored Easy (no-code, owner can deploy in 1-2 hours), Medium (4-6 hours, may need one Zapier or Make scenario), or Hard (10+ hours, needs custom prompt engineering). Implementation effort is in person-hours, not calendar time.

The top 5 opportunities

1. Proposal/SOW first-draft from discovery-call notes

The current pain: After every initial discovery call, you spend 60-90 minutes turning the conversation into a structured proposal — restating the problem, sketching the engagement scope, dropping in your standard pricing language, formatting in your template. At your current cadence of ~2 discovery meetings per month, that's 1.5-3 hours every month going to drafting. The work is high-judgment for the first 20 minutes (figuring out the actual ask) and low-judgment for the next 60 (formatting, restating, copy-paste from past proposals) — exactly the kind of split that automates well.

The automation: Record discovery calls with Otter.ai or Fathom (whichever you already use; Fathom is free for the first 5 hours/month). Pipe the transcript into Claude using the prompt below. Claude returns a first-draft SOW that's about 70% of the way there — you spend 15 minutes editing instead of 90 writing.

Sample prompt (paste into Claude with the transcript):

You are a senior consultant at Nexus Point Group. Convert the discovery-call transcript below into a first-draft Statement of Work using exactly this structure:

1. Engagement summary (3 sentences)
2. Stated client objective (in client's words, lifted from transcript)
3. Proposed scope (bulleted, 4-7 items, each starting with a verb)
4. Out of scope (bulleted, 3-5 items — be explicit about what we're NOT doing)
5. Deliverables (bulleted, with a 1-line description each)
6. Timeline (in weeks; mark assumed dependencies)
7. Investment (use $TBD placeholders — I'll fill these in)
8. Acceptance criteria (3-5 specific tests)

Keep the tone direct and operator-grade. No marketing language, no "leverage" or "synergy." If a section can't be filled from the transcript, write [NEEDS CLARIFICATION: question for client] instead of inventing.

Transcript:
{paste here}

ROI math: 1.5 hr × $250/hr = $375/mo today. Setup pays back the first time you use it. Scales to ~$1,250/mo at 4-6 SOWs per month. Even if 50% of drafts need a full rewrite, the math still beats the setup cost in week one.

Risk to watch: Don't paste client transcripts into the consumer ChatGPT product. Use Claude's API or the Claude Team plan, both of which don't train on your inputs. For higher-stakes engagements, sign Anthropic's standard MSA before using.

2. Pre-meeting research briefs

The current pain: Before each first-time client call, you spend 30-45 minutes reviewing their LinkedIn, their website, recent news, and any common-thread connections. You take rough notes, decide your hooks, and walk in. At ~2 first-time meetings per month, that's about an hour of prep monthly. This work is high-leverage (it's what makes you sound prepared) but feels low-judgment — and as meeting cadence grows it's the first thing that gets cut.

The automation: Build a one-prompt brief generator. Inputs: the prospect's name, company, and LinkedIn URL. Output: a 1-page brief covering company snapshot, role/tenure, recent posts/activity, mutual connections (if you copy LinkedIn's network panel), 3 likely talking points, and 2 risks. Run it 30 minutes before each call.

Sample prompt:

You are preparing a pre-call brief for a Nexus Point Group consultant.

Inputs:
- Prospect: {name, title, company}
- Company: {website summary, headcount, recent news if any}
- LinkedIn highlights: {paste 5-10 most recent posts/comments}

Produce a one-page brief in this exact structure:
- Company snapshot (3 bullets)
- Person snapshot (3 bullets)
- 3 likely talking points (each: a hook + why it lands)
- 2 risks (anything that suggests we're a bad fit, or anything sensitive to avoid)
- Suggested first question to open the call

No fluff. If a section is sparse, say "limited public signal" rather than padding.

ROI math: 0.75 hr × $250/hr = $190/mo today (2 meetings/mo). At 6 meetings/mo (the typical scale of an active practice), 2.25 hr × $250 = $565/mo. The bigger second-order benefit isn't dollars — it's that you walk into every call as the most prepared person in the room, which lifts close rate.

Risk to watch: Don't read from the brief during the call. Use it before the call, then close it.

3. Weekly client status reports

The current pain: For your one active engagement, you draft a weekly status email summarizing what happened, what's blocking, and what's next. ~30-40 minutes × 4.3 weeks = ~2 hours/month. Right now that's tractable manually — but it's the workflow that punishes you most as engagements stack. By the time you're at 4 active clients, status reports are 7+ hours of every Friday and they're the first thing that gets pushed to Monday or skipped.

The automation: Set up a Notion or Google Doc page per client where you drop bullet-point notes throughout the week (5 seconds per drop). On Friday morning, run a Make.com or Zapier scenario that grabs each client's bullets, sends them to Claude with a templated prompt, and emails you a draft status update per active client. You spend 5-10 minutes editing instead of 30-40 writing — and the architecture works whether you have 1 client or 10.

Sample prompt (run per client per week):

You are writing a weekly status update from Nexus Point Group to a client.

Tone: confident, specific, no jargon, no hedging. The client should finish reading and know (a) what we did, (b) what they need to do, (c) when the next visible thing happens.

Context (this week's raw notes):
{bullet points dropped during the week}

Output structure:
- Subject line (one line, specific number or milestone if possible)
- "What we shipped this week" (3-5 bullets, past tense, with concrete artifacts)
- "What we need from you" (1-3 items, each with a specific ask and deadline)
- "Coming up next" (1-2 items, named outcomes with dates)
- One-sentence sign-off

Length cap: 220 words. If raw notes are sparse, mark sections [thin — verify before sending] rather than padding.

ROI math: 1.7 hr × $250/hr = $425/mo today (1 active client). 6.8 hr × $250/hr = $1,700/mo at 4 active clients. Setup is justified now precisely because the marginal cost of adding clients to the system is near zero.

Risk to watch: The "[thin — verify before sending]" guard is non-negotiable. Sending an automated status with hallucinated milestones is the fastest way to lose a client.

4. Inbound lead qualification + first-touch reply

The current pain: You get inbound interest from LinkedIn DMs, your contact form, and email. Some are real (right size, real budget). Many aren't (vendors pitching you, students, off-fit). You triage manually, which costs both time and inbox latency — and at your current cadence the latency is the bigger issue. With ~2 discovery meetings per month, every inbound that goes cold because you replied 3 days later is a meaningful slice of your pipeline.

The automation: A Cal.com qualification form (4-5 questions: company size, role, budget range, timeline, problem in 2 sentences). Form responses pipe into Claude via Zapier with a prompt that scores the lead Hot/Warm/Cold and drafts a tailored first-touch reply. Hot/Warm get auto-sent or flagged for your review; Cold get a polite templated decline.

Sample prompt:

You are triaging an inbound lead for Nexus Point Group, a small management consulting firm.

Lead intake:
{form responses}

Step 1 — score the lead Hot/Warm/Cold:
- Hot: company 50+ employees, defined budget $25k+, problem in our wheelhouse, decision-maker
- Warm: smaller company OR vague budget OR adjacent problem
- Cold: vendor pitch, student, fully off-fit problem, no budget

Step 2 — draft a reply tailored to the score:
- Hot: propose a 30-min call this week, suggest 2 specific times, mention one specific thing from their form that you understood
- Warm: ask 1-2 clarifying questions before booking
- Cold: polite decline, suggest 1 alternative resource

Output JSON: {"score": "Hot|Warm|Cold", "reply_draft": "..."}

ROI math: 1.5 hr × $250/hr = $375/mo today on the time savings alone. The second-order benefit is the real prize at your current scale: 1 extra closed engagement per quarter from faster reply × $2k average = $667/mo amortized. Combined: ~$1,000/mo today. At higher inbound volume, time savings alone double to ~$750/mo.

Risk to watch: Always review Hot replies before they send for the first 2 weeks. After that, auto-send is fine.

5. Searchable knowledge base across past engagements

The current pain: You've delivered dozens of engagements and you remember 70% of what's in them. When a new prospect asks "have you done something like X before?" you spend 20-40 minutes hunting through old Google Drive folders, copying snippets, reformatting. Sometimes you give up and write a fresh response, leaving past work on the table.

The automation: A retrieval-augmented (RAG) Claude assistant trained on your past deliverables. Setup: upload your past SOPs, decks, status emails, and case studies to a vector store (the cheapest credible option in 2026 is using Claude's Files API with prompt caching — $0.30/GB/month). Query in plain English: "What did we do for [client X] in 2024 around go-to-market?" and get a grounded answer with citations to the source docs.

Sample SOP (handoff to a Fiverr/Upwork freelancer):

1. Collect all past client deliverables in one folder, anonymize client names if needed
2. Upload to Anthropic Files API (or pgvector + OpenAI embeddings if you prefer)
3. Build a Claude system prompt that says: "Answer using only the uploaded docs. If the answer isn't in the docs, say so. Always cite the doc name and date."
4. Wrap it in a one-page Streamlit or Retool app that you bookmark and use daily

ROI math: 2 hr × $250/hr = $500/mo today on time savings (and even that is generous given you can still hold most past work in your head at 1-engagement scale). The "extra deal from citing past work" upside at $2k average is ~$667/mo amortized for one extra deal per quarter — real but not enough to justify Hard difficulty today. Becomes a clear win at 5+ engagements / month.

Risk to watch: Don't give the assistant access to current confidential deliverables in flight. Only past, completed engagements where confidentiality has lapsed or been waived.

Suggested rollout order

Week 1: Ship #1 (Proposal drafts) and #2 (Pre-meeting briefs). Both are Easy, both pay back the first time you use them. Combined recovery today: ~2.25 hr/mo (~$565/mo); scales to ~7.25 hr/mo (~$1,815/mo). Spend 3 hours total on setup; the prompts are reusable as cadence grows.

Week 2: Ship #3 (Status reports). Medium difficulty. At 1 active engagement the daily savings are small (~1.7 hr/mo) — the reason to ship it now anyway is that the architecture is what matters. By the time you're at 4 clients, the savings hit ~7 hr/mo and you're glad you didn't bolt this on under pressure. Budget 3 hours.

Week 3-4: Ship #4 (Lead qualification). Medium difficulty. The form + scoring is fast; the auto-reply review window slows it down. Don't skip the manual-review window — 2 weeks of flagged drafts is the cheapest QA you'll ever do. Biggest lever today: faster reply lifts close rate when monthly meeting count is small.

Month 2 or later: Decide on #5 (Knowledge base). At your current scale this is the lowest-ROI / highest-effort item — defer until you have 5+ active engagements or noticeably struggle to remember past work. When you do ship it, hire 4 hours of Fiverr help with the SOP above rather than building it yourself.

Total at current scale, fully shipped: ~$2,500/mo (including the lead-speed second-order benefit). Total at scaled state (4 engagements / 6 meetings): ~$5,000/mo. The difference is mostly status reports and proposals — both linear in your client/meeting count.

What we'd handle if you wanted help

We built the audit. We can build the systems. Same person, no rehiring. A 6-week implementation retainer at $1,500/mo ships #1-#4 fully wired, gets you 70% of the way through #5, and leaves you with documented runbooks for everything we touched. A 30-minute call to scope is included free if you reply within 7 days of receiving this report.

Appendix: tools you'd need

Estimated monthly software total: $35–145. Even at the high end, payback is in week one.