Professional Services · Revenue & Growth
Productized Lead Qualification for Consulting
consultancies, transformation offices, strategy teams, and boutique advisory firms usually arrive here with two questions: what does AI-native lead qualification actually ship, and what does it cost. Both are answered below, alongside the operating posture and the governance frame.
Projects from $15k · Refundable 7 days · Kickoff within 5 days
Early access: we work with a small first cohort. Engagements are scoped, priced, and shipped end-to-end by our team — not referred to third parties.
In one sentence
AI-native lead qualification for consulting — A phased engagement that ships a production lead qualification workflow on top of knowledge bases and CRM, moves the operating metric against a Discovery-captured baseline, and is operated under explicit governance from day one. Expected delta on speed to lead: −77%.
Key facts
- Industry
- Consulting
- Use case
- Lead Qualification
- Intent cluster
- Revenue & Growth
- Primary KPI
- speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction
- Top benchmark
- Cost per qualified meeting: $420 → $95 (−77%)
- Systems integrated
- knowledge bases, CRM, project management
- Buyer
- consultancies, transformation offices, strategy teams, and boutique advisory firms
- Risk lens
- client confidentiality, weak analysis, over-automation, IP handling, and recommendation quality
- Engagement timeline
- Discovery 2 weeks → Build 9 weeks → Run continuous (integration-heavy)
- Team size
- 1 senior delivery + 1 part-time domain SME
- Discovery price
- $5k · 2-week sprint
- Build price
- $15k–$22k · 6-8 weeks
Primary outcome
separate serious buyers from noise faster
What we ship
AI qualification assistant, scoring rubric, routing rules, and CRM governance
KPIs we report on
speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction
Why Consulting teams hire us for this
Consulting buyers we talk to share a common frustration: too many AI vendor demos, too few production deployments that survive a quarterly review. AI-native lead qualification is the answer to that gap — every engagement we ship is designed to pass a CFO's challenge, a risk officer's review, and an operator's daily use, simultaneously.
Recent industry benchmarks (Gartner, Salesforce Research) show consulting revenue teams spend 60-70% of their week on non-selling activities. AI-native delivery targets that non-selling block first.
Industry context: Mid-market and enterprise operators face the same fundamental tradeoff: AI must compress operational cycle time while remaining auditable and integrable with existing systems of record.
Benchmarks we hit
Reference benchmarks from production deployments of lead qualification in consulting-comparable contexts. Sources noted per row. Your actuals are measured against the baseline captured in Discovery.
| Metric | Industry baseline | AI-native typical | Delta |
|---|---|---|---|
Cost per qualified meeting Includes AI infra cost, SDR time, and overhead allocation | $420 | $95 | −77% |
Lead-to-meeting cycle time Median across Salesforce-reporting B2B teams; AI-native compression validated on first thin-slice deployment | 11.4 days | 2.8 days | −75% |
Outbound reply rate Industry baseline from Gartner B2B Sales Pulse; AI-native lift from per-prospect context injection | 1.2% | 4.1% | +3.4× |
Benchmarks are reference values from comparable engagements and authoritative sector benchmarks. Your engagement's baseline is captured during Discovery and actuals are reported weekly during Run against that baseline.
How we operate the workflow
The architectural choice that defines the operating model for lead qualification in consulting is not the model — it is the case representation. A case is the atomic unit of work the system processes: a ticket, a record, a claim, a request, a transaction. We design the case shape during Discovery, instrument every state transition during Build, and operate the workflow against that case-level telemetry during Run. Case-level telemetry is what makes the workflow legible to consulting leadership; it is also what lets us detect drift early.
What we build inside the workflow
The Build deliverable for lead qualification in consulting is not a model — it is an operating system around a model. The model is the cheap part (Claude or GPT-4-class, swappable). The operating system — eval harness, reviewer queue, audit log, governance map, runbook — is the expensive part, and the part that determines whether the workflow survives the second quarter of production.
Reference architecture
4-layer AI-native workflow for revenue & growth
The architecture is designed for substitution: any single layer (model, retrieval store, reviewer UI, action client) can be swapped without rewriting the others. That is the property that lets lead qualification survive 12+ months of provider and pricing change.See the full architecture diagram for Revenue & Growth →
AI-native vs traditional approach
How a scoped AI-native engagement compares to the alternatives for lead qualification in consulting: in-house build, BPO retainer, generic SaaS subscription, traditional consulting engagement.
| Dimension | Traditional (in-house build or BPO) | AI-native engagement (us) |
|---|---|---|
| Time to production | Two quarters minimum | Production traffic within 6-10 weeks |
| Pricing model | FTE hourly retainer or fixed staffing | Three independent commercial envelopes |
| Audit / governance | Document-driven, periodic snapshot | Runtime guardrails + audit log + governance map + quarterly attestation |
| Operator throughput lift | 1.0× (baseline) | −75% |
| Cost per unit | Linear with operator headcount | Typically 60-80% lower |
| End-of-engagement | Multi-quarter notice + knowledge loss | Month-to-month Run, full handover plan in Build SoW |
Traditional process automation projects cost $80-200k+ with 6-12 month payback; AI-native engagements deliver thin-slice production in 6-8 weeks with measurable baseline-vs-actuals reporting.
Engagement scope & pricing
We run this as a fixed-scope engagement with a clear commercial envelope, not an open-ended retainer.
Revenue engagement
Three phases, billed separately. You commit one phase at a time.
Phase 1 · Discovery
$5k
2-week sprint
Phase 2 · Build
$15k–$22k
6-8 weeks
Phase 3 · Run
$2k–$3k / mo
optional, hourly bank also available
~$25k–$45k typical year 1 (60% take the run option for ~6 months)
Outbound, growth, or revenue-ops workflow, integration with your CRM, weekly operating review during Run.
The only thing you commit to today is the Discovery sprint. The Build SoW is produced inside Discovery and you decide whether to proceed. Run is optional.
The 4-phase delivery model
Phase 1 · Weeks 1–2
Discovery
Workflow mapping, integration scoping, baseline capture, risk register, labelled-test-set seed. The output is the Build SoW with a fixed price and named deliverables.
Phase 2 · Weeks 2–4
Design
Architecture sprint covering the four-layer workflow (intake, context, action, review), the integration footprint, the evaluation methodology, the reviewer UX, and the governance map.
Phase 3 · Weeks 4–8
Build
6-10 week sprint that ships the thin-slice production workflow on top of your existing systems. Eval harness gating every prompt change. Reviewer queue staffed. Audit log queryable. Dashboard live.
Phase 4 · Weeks 8+
Run
We run the workflow with you weekly, expand into adjacent work, and report against baseline.
Interactive ROI calculator
Estimate your AI-native ROI for lead qualification
Reference inputs below are typical for consulting teams in the revenue cluster. Adjust them to match your situation.
Projected
Current monthly cost
$24,000
AI-native monthly cost
$7,920
Annual savings
$192,960
67% cost reduction · ~468 operator-hours freed / month
Governance and risk controls
AI-native workflows need a risk model that fits the sector. In consulting, the central concerns are client confidentiality, weak analysis, over-automation, IP handling, and recommendation quality. We ship five controls on every engagement: every answer or recommendation is grounded in approved sources; the system keeps a record of inputs, outputs, model versions, and reviewers; low-confidence or high-impact cases route to humans; quality is measured with a labelled test set of real examples; your team owns the final policy and escalation rules.
How we report ROI
ROI on lead qualification compounds through four channels: labor leverage (same team, more volume), quality consistency (fewer missed steps, less rework), cycle-time compression (decisions and handoffs happen faster), and learning speed (every case improves the taxonomy and playbook). In consulting, that shows up in utilization, delivery margin, proposal win rate, research cycle time, and client satisfaction.
Selected portfolio
Real builds — lead qualification in consulting and adjacent sectors
Below are engagements drawn from our active portfolio where the workflow rhymed with lead qualification in consulting or in adjacent contexts. Scope and stack are accurate; client identities are withheld under engagement NDAs.
Q2 2026
Digital brand refresh + integrated recruitment platform for an IT consulting firm
Enterprise IT consulting boutique · Europe
Repositioning + redesign for a pure-staffing IT consulting house serving CIO buyers. Editorial architecture tightened around three expertise pillars (IT & SAP, cloud, cybersecurity), premium art direction, conversion-oriented UX, marketing-team-owned Sanity CMS, and an integrated recruitment funnel for senior consultant sourcing.
- Next.js + Framer Motion
- Sanity CMS (marketing-owned)
- Recruitment funnel
Q1 2026
Premium bilingual corporate site + internal CRM
Multi-vertical consulting group · Europe
Corporate marketing site with animated bento-grid editorial, bilingual content architecture, and an internal CRM behind the scenes for lead handling. Designed to project a premium positioning aligned with enterprise buyers while keeping marketing-team ownership of the content layer.
- Next.js + animated bento grids
- Bilingual content layer
- Internal CRM integration
Q1 2026
AI pricing system for startup founders — 9-step foundation + personalised AI brain
Founder-led pricing-strategy AI SaaS · DACH
First AI-powered pricing platform for startup founders. Structured 9-step pricing-foundation flow (product, customers, competition, costs, boundaries, model, strategy), personalised AI brain that learns from each business over time, two subscription tiers with money-back guarantee. Built end-to-end including billing, AI orchestration, and onboarding.
- Next.js + TypeScript
- Multi-LLM orchestration
- Subscription billing
Client identities withheld under engagement NDAs. Sector, geography, and scope are accurate. Full case studies on request.
Common pitfall & mitigation
The failure mode we see most often on AI-native lead qualification engagements in consulting contexts.
Attribution loss
AI-generated touches blur the funnel; nobody knows what really worked
UTM convention + touch-level logging from day 1; weekly cohort analysis in the Run review
Why digital-native teams hit a different ceiling on this
The consulting engagement model for lead qualification is built around a hard constraint: your engineers will read every line of code we ship, and the line they would not have written themselves is the line that becomes the conversation. We design for that conversation from day one.
The prompt layer is documented at the rationale level, not just the syntax level — why this structure, why this retrieval shape, why this confidence threshold. The evaluation harness is structured as a test suite your team would write if they had three months to think about it. The reviewer UI is a React app with explicit state management, not a black box. The deployment pipeline is your existing CI, with our additions as standard GitHub Actions or equivalent. The artefacts we ship are the artefacts a senior engineer at your team would have shipped, with the prompts and evaluation discipline as the differentiator.
What we bring that your team would have spent six months reinventing is the operational discipline around the model layer. Prompt versioning that survives team turnover. Retrieval freshness that survives data-source schema drift. Reviewer queues that survive scale. Model swapping that survives provider outages. We have shipped the pattern enough times to know which pieces fail under real production load, which pieces look good in a slide deck and break in week three, and which pieces compound value over a year of operation. That experience is the engagement, not the code.
The concrete first-30-day delivery plan
Most consulting AI projects fail in the first month for the same reason: too much time in scoping, too little in shipping. Our Build phase inverts that ratio deliberately. Week 1 has running code; week 4 has reviewable thin-slice production traffic; week 6 has a defensible accuracy baseline against the labelled test set.
The shape of the first week is opinionated. By end of day Wednesday, the retrieval index is loaded with the first batch of approved sources. By end of day Friday, the intake classifier is hitting the labelled test set with an initial accuracy number. The number is intentionally not impressive — it is a baseline against which weeks 2 and 3 measure progress. Most teams underestimate how motivating that early concrete number is for both the operator team (it stops feeling abstract) and the engineering team (the eval feedback loop is closing).
From week 2 onward the cadence is metric-driven. Every Friday produces a delta report against the labelled test set: which slices improved, which regressed, what the next iteration targets. The operator team participates in the Friday review; their judgment on edge cases becomes the next iteration's prompt or retrieval tweak. By week 6, the system has been through 12-15 evaluation cycles, each with consulting-specific calibration, each tied to a documented change. The workflow that hits production at the end of Build is the workflow that has survived a month of empirical correction, not the workflow that looked good in the architecture diagram.
Our Build cadence on lead qualification for consulting is bias-corrected against the two failure modes we have seen kill consulting AI projects most often: scoping that drifts week-by-week, and a labelled test set that arrives in week 6 instead of week 1.
We fix the scoping by signing the Build statement of work before any code is written — the deliverables are named, the integration footprint is bounded, the milestones have dates. We fix the labelled test set timing by treating it as the week-1 deliverable. Week 1 is not "scoping week" — it is "labelled-test-set week", because every subsequent engineering decision is measured against that test set.
Week 2: retrieval index live with first batch of approved sources. Week 3: intake classifier scoring against the test set, first calibration report. Week 4: action layer drafting with reviewer approval; first end-to-end case flow. Week 5-6: thin slice in production on 5-15% of routine consulting traffic, first weekly review with the operator team. Weeks 7-10: production envelope widens case-class by case-class, calibration loop tunes against the empirical evidence, exceptional cases route to enriched escalation. By day 60-70, the workflow is operating at its target envelope.
Closest precedent in our portfolio
A comparable engagement worth knowing about for lead qualification in consulting is summarised below. Identity withheld under engagement NDA; sector and stack are accurate.
Digital brand refresh + integrated recruitment platform for an IT consulting firm. Repositioning + redesign for a pure-staffing IT consulting house serving CIO buyers. Editorial architecture tightened around three expertise pillars (IT & SAP, cloud, cybersecurity), premium art direction, conversion-oriented UX, marketing-team-owned Sanity CMS, and an integrated recruitment funnel for senior consultant sourcing. (Enterprise IT consulting boutique · Europe, Q2 2026.)
The architectural choices that worked there translate to consulting lead qualification with two adjustments: the data-source mix shifts to match your operating systems (knowledge bases, CRM, and adjacent), and the reviewer SLAs adjust to your team's operating cadence. The four-layer pattern (intake, context, action, review), the evaluation discipline, and the audit posture are portable.
For US buyers
US compliance scaffolding for lead qualification in consulting (NIST AI RMF)
Consulting engagements touching US clients on lead qualification ship with the regulatory scaffolding your procurement, compliance, and legal teams expect. The framework that matters most for consulting is NIST AI Risk Management Framework (AI 100-1) (NIST AI RMF) — addressed below alongside the adjacent frames we encounter.
NIST AI RMF
NIST AI Risk Management Framework (AI 100-1)
Authority: U.S. National Institute of Standards and Technology
- Scope
- Voluntary framework: Govern, Map, Measure, Manage functions for AI system risk.
- How we ship inside it
- Every engagement maps to NIST AI RMF during Discovery. The control map produced becomes the artefact your internal audit and security teams use to defend the workflow.
Premium engagement page · hand-edited
The bespoke playbook for this combination
AI-augmented business development for management consulting — opportunity scoring, proposal pre-draft, RFP triage.
Architecture, end-to-end
BD workflow AI for consulting firms — inbound opportunity scoring on fit / firm capability / win probability, proposal pre-drafting from past similar engagements, RFP triage with response-effort estimation.
Opportunity ingest (CRM + email RFP intake) → AI scoring on fit + capability + win probability → partner queue with similar past engagement references → proposal pre-draft from confidentiality-tagged corpus → response-effort estimator.
Specific risks we engineer against
The four to six failure modes we have actually encountered on engagements that look like yours. Each has a documented mitigation in the Build SOW.
RiskCross-client confidentiality leak via past-engagement retrieval
MitigationConfidentiality tags enforced at retrieval; cross-client leak impossible by design.
RiskPartner over-relies on AI scoring
MitigationScore is advisory; partner decision logged; quarterly calibration audit.
Reference deltas on consulting BD engagements
| Metric | Before | After | Window |
|---|---|---|---|
| Proposal prep time | 15–30 hours | 4–8 hours | 30 days |
| Win rate on qualified opps | Baseline | +15 to +25% | 120 days |
Reference from mid-market consulting firms.
Objections we hear most often
Will partners actually use it?+
We co-design with 2 senior partners during Build; adoption metric tracked weekly.
Mini SOW
What the Build SOW looks like
Total fee
$22,000 Discovery + Build
Duration
8 weeks to thin-slice production
Week 1–2
Discovery: opportunity corpus + win/loss audit + confidentiality tags.
Week 3–5
Scoring + proposal pre-drafter.
Week 6–8
Partner queue + production rollout.
Procurement FAQ
Cross-client confidentiality?+
Tags at retrieval prevent cross-client leak.
Real shipped systems
What our clients say
Below: attributions from active clients. Client identities are withheld in public form pending written approval; live references available to qualified procurement contacts on discovery call.
AI SaaS · DACH region
“They shipped the production version of our pricing brain in 6 weeks, including the billing layer and the onboarding flow. We had been bouncing between contractors for 4 months before.”
Founder, AI Pricing SaaS
Outcome: From 0 to live SaaS with paying customers in 6 weeks. Production billing live, AI onboarding flow shipped, 2 pricing tiers active.
Government-licensed legal services platform · GCC region
“A complete bilingual platform compliant with regulator requirements. Technical quality and delivery speed are outstanding.”
Founding team, regulated legal marketplace
Outcome: Ministry-of-Justice-licensed national legal marketplace, EN/AR bilingual, in 16 weeks. Directory + bookings + legal tools + emergency contacts.
Property management operator · GCC region
“We replaced spreadsheets and 4 disconnected tools with a single OA platform. 55 screens, 47 tables, a voting platform, and an internal portal — all on the same identity layer.”
CTO, multi-region property operator
Outcome: Centralised property operations across multiple owners associations. 14-week first release; 8-week follow-on for the staff portal; 6-week follow-on for e-voting.
Before / after
Concrete deltas from shipped engagements
Owners-association management workflows
Property management operator · GCC
Operator was scaling association count and could not maintain manual coordination. Replaced 4 fragmented tools with a single AI-augmented operational backbone.
Metric
Operational surface area
Before
Fragmented across spreadsheets + email + 4 SaaS tools
After (14 weeks Build phase)
Unified SaaS with 55 screens / 47 normalized tables / cross-app identity
Pricing strategy SaaS onboarding
AI pricing SaaS · DACH
Founder shipping AI-native pricing platform for early-stage SaaS. Discovery + Build delivered a working SaaS with subscription billing and an AI brain that learns from each customer.
Metric
Time-to-pricing for a new founder
Before
3–4 weeks of consultant time + spreadsheets
After (6 weeks total Build)
9-step structured AI workflow, completed in 30–45 minutes
Lawyer discovery and appointment booking
National legal marketplace · GCC
Regulated entity needed to launch the national reference platform for legal services. Delivered a Next.js 16 monorepo with bilingual content layer, PDF generation, and police directory.
Metric
Citizen access to certified legal services
Before
Fragmented across social media, no central directory, phone-only booking
After (16 weeks Discovery + Build)
Ministry-licensed bilingual EN/AR marketplace; multi-channel booking; legal tools; emergency hotline
Marketing site + booking funnel
Premium vehicle care specialist · DACH
Niche detailing workshop needed to project premium positioning matching their workmanship. AI-assisted copywriting + image art-direction compressed launch time.
Metric
Brand perception alignment
Before
Generic web presence — did not match workmanship quality
After (3 weeks concept-to-live (AI-augmented build))
Premium responsive site, German-market SEO foundation, appointment-oriented CTAs
For US companies
Start a US-friendly engagement
Discovery from $8,500–$12,000, Build from $35,000–$75,000, optional Run from $5k/mo. Fixed-price, milestone-billed, you own every artefact. Send a short brief and we reply within 5 business days. 11am–4pm ET overlap for live syncs.
USD pricing
Discovery $8,500–$12,000 · Build $35,000–$75,000
US-style commercial
MSA / SOW / mutual NDA standard. DPA with SCCs included.
Limited capacity
We onboard 3–5 new clients per quarter to protect delivery quality.
Build internally or work with us
The opportunity cost of building first in consulting is often invisible: 6-9 months spent hiring, tooling, and converging on a reference architecture is 6-9 months of competitors shipping. The engagement model we propose front-loads the reference architecture and the senior delivery team, then transitions the operation to your team once the pattern is proven.
What to ask us before signing
- Ask for a workflow map that shows intake, retrieval, generation, review, escalation, system updates, and measurement.
- Ask for an evaluation plan using real examples from consulting, not only generic test prompts.
- Ask how we will move speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction within the first 30 to 60 days.
- Ask which parts of the process remain human-owned and why.
- Ask for our exit plan: what stays with you if the engagement ends.
Recommended first project
Our recommendation for a first lead qualification engagement in consulting is to pick the slice of the workflow that satisfies four criteria: there is a measurable baseline, the work is genuinely repetitive, the failure mode is reversible within a reasonable window, and a senior operator on your team can be the first reviewer. Those four criteria filter out the engagements that look impressive in a slide and fail in week three. The 90-day target is "thin slice in production with a defended baseline". By day 30, the system processes a small share of real traffic with full reviewer oversight. By day 60, the share has widened and the calibration is data-driven. By day 90, the operating cadence is your team's, the dashboard reflects empirical performance, and the case for the next workflow writes itself.
Frequently asked questions
How do you automate lead qualification in consulting with AI?+
Three phases. Discovery (2 weeks) produces the labelled test set, the system map, and the Build statement of work. Build (6-10 weeks) ships a thin-slice production deployment on top of knowledge bases and adjacent systems, with versioned prompts and a reviewer queue. Run (optional, month-to-month) operates the workflow weekly against speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction.
What does it cost to automate lead qualification for consulting teams?+
Three phases, billed separately. Discovery sprint: $5k (2-week sprint). Build engagement: $15k–$22k (6-8 weeks). Run retainer: $2k–$3k / mo (optional, hourly bank also available). ~$25k–$45k typical year 1 (60% take the run option for ~6 months). Outbound, growth, or revenue-ops workflow, integration with your CRM, weekly operating review during Run.
What is the best AI agent for lead qualification in consulting?+
There is no single "best" off-the-shelf agent for lead qualification in consulting — the right architecture depends on your knowledge bases setup, your data, and your risk profile. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for knowledge bases and CRM integrations, and a reviewer queue. We benchmark candidate models against a labelled test set during Discovery and pick the one with the best accuracy/cost ratio for your workflow.
How long does it take to deploy AI lead qualification for consulting?+
End-to-end lead time from kickoff to thin-slice production: 6-10 weeks. End-to-end to full operating envelope: 10-14 weeks. speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction is instrumented from day one of Build; the dashboard goes live by week 4-5; production traffic starts by week 6-8. By 90 days, leadership has a 30-60 day record of operating performance against the Discovery baseline.
What do we own, and what do you own?+
We own the workflow design, the prompts, the retrieval architecture, the evaluation harness, and weekly improvement. Your consultancies, transformation offices, strategy teams, and boutique advisory firms team owns data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
What's the revenue ROI shape for lead qualification in consulting?+
speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction is the bridge metric to utilization, delivery margin, proposal win rate, research cycle time, and client satisfaction. The first 30 days are negative (engagement cost vs. limited production volume); month 3 typically hits break-even; months 4-12 are strongly positive as the labelled test set grows and the prompt library tunes to your category.
Do you train models on our data?+
No. We do not train any model on client data. Anthropic Zero-Data-Retention is enabled by default; OpenAI default-no-training is honoured. Prompts, retrieval indexes, audit logs, and integration data live in your cloud account under your IAM. At engagement end, every artefact transfers to your repository.
What if we want to exit the engagement?+
Discovery and Build are fixed-scope, so there is no mid-engagement exit cost. Run is month-to-month with 30-day notice. Every artefact (prompts, eval harness, integration code, dashboards, runbooks) is in your repository throughout the engagement, not behind our SaaS. There is no lock-in.
What does success look like 90 days after Build closes?+
speed to lead, MQL to SQL conversion, sales acceptance rate, and wasted meeting reduction measurably improved against the Discovery baseline. Your team is operating the workflow with the cadence we shipped during Build. The audit log is queryable. The reviewer queue is calibrated. The next workflow scope is informed by real production evidence rather than initial assumptions.
What support is included after the engagement ends?+
Optional Run retainer covers weekly cadence, prompt refresh, retrieval index updates, and reviewer-queue calibration. Architecture-level questions and breaking-change support are billed hourly outside of Run. Most engagements transition Run in-house at month 6-12; we stay available for architecture decisions for 12 months at no extra charge.
How does this integrate with knowledge bases and our existing stack?+
Discovery scopes the integration footprint explicitly. We integrate at the API layer; no replatforming required. The Build statement of work names exactly which systems are connected, which data flows are bidirectional, and what authentication patterns we use (SSO, service accounts, OAuth scopes). The integration code lives in your repository.
What does your team look like during an engagement?+
Discovery: 1 senior delivery lead + 1 PM, ~30 hours/week. Build: 1 senior delivery lead + 2-3 senior AI engineers, ~50-80 hours/week across the team. Run: 1 delivery owner + 1 engineer on weekly cadence. We do not use offshore staff augmentation. Every engineer touching your engagement is senior-level.
Sources we reference
The following sources inform the architecture, governance, and benchmarks we apply on consulting engagements. Cited here so you can verify and dig deeper.
- OECD AI Policy Observatory
- AI Index Report — Stanford HAI
- The State of AI — McKinsey & Company
- B2B Sales Pulse Survey — Gartner for Sales
- State of Sales Report — Salesforce Research
- Google Search Central: helpful, reliable, people-first content
- Google Search Central: URL structure best practices
High-intent reads
Start the engagement
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Tell us about your workflow, the systems involved, and the KPI you want to move. We'll send a scoped statement of work within 5 business days.