Your teams run Copilot, ChatGPT, and a few automation tools, but nobody owns the decisions. Our AI consulting services for mid-market businesses help you choose what to buy, build, scale, or stop, then take what works to production.
We score each idea on business value, feasibility, risk, and effort. You leave with initiatives worth funding and a clear call on what to buy, build, integrate, or stop.
We design the production path before the build starts: what the pilot connects to, who owns it, and which KPI decides if it scales.
We map your AI spend and spot duplicate tools. Then we help your team settle on a single standard toolset and a consistent level of skill.
You don’t have to — we set priorities and governance while you do the work. Knowledge transfer is part of the deliverables, so ownership moves inside over time.
Each stage of our AI consulting has its own price, timeline, and exit point. Start where you are.
We review your processes, data, technology, people, security, and governance in a free AI Maturity Assessment. You get a baseline before you commit budget or pick a direction.
3–4 weeks, fixed fee. We map workflows across departments and score every opportunity by impact and effort. You leave with a 90-day roadmap and a clear call on what to do.
30 days, fixed fee. We build a working AI Pilot for your strongest use case and test it with real users. We always set business KPIs so the pilot ends with a funding decision.
Scoped estimate after the pilot. We integrate proven use cases into your CRM, ERP, and core systems, then add security, evaluation, and monitoring.
We bring your developers to one secure AI toolset and a spec-driven workflow, with human review at every stage of the AI SDLC.
A prototype shows that an idea can work. Production has to prove more: security, repeatability, cost, ownership, and adoption. That gap is where most AI experiments stall.
Each pilot needs one primary KPI, measured before work starts, for example, cost per resolved inquiry in support or reporting time in finance. Guardrail metrics make sure quality doesn’t drop to hit it.
Before anyone sees the numbers, we agree on which results mean you scale, iterate, or stop. That keeps a weak pilot from surviving on money already spent.
Before go-live, we name who owns quality, incidents, model changes, and business results. Then we train them and hand the system over.
1. Zipify’s flagship Shopify app was over ten years old. AI tools were in place, but results depended on the engineer, and a new product could take three months to reach a customer.
2. We set up one secure AI toolset, introduced spec-driven development with human sign-off at each stage, and brought every engineer to the same baseline.
Result: 12x faster time to a working prototype, up to 4x faster delivery of some features, and a feature estimated at 3+ months shipped in a week.
1. The poker training platform’s developers used AI tools, but a medium-sized feature still took about a month, and subtle AI-written bugs reached players.
2. We wrote a detailed spec for each part of the product and connected design, specs, and AI agents into one workflow. Every AI-built version gets human review in an isolated environment.
We proved it on two live feature pages, then set it up in GTO Lab’s repository and trained the team to run it.
Result: Active work on medium-complexity features dropped from about 9 days to under 4.5. GTO Lab reports zero AI-caused production bugs since the switch.
We’ve been in business for 20+ years and have been building enterprise conversational AI since 2016, before most of today’s platforms existed.
We’ve turned clients away after running the numbers because the build wouldn’t pay for itself. We don’t sell a platform, so we recommend the stack that fits your budget.
Clients tell us we feel like a senior internal team rather than a vendor. The people who plan your roadmap also build what’s on it, and the same team stays with you from assessment to production.
We run a free maturity assessment of your processes, data, technology, people, and governance.
In a 3–4-week AI Compass Sprint, we rank opportunities by value, effort, and risk, and set the baseline KPI.
In 30 days, we build a working pilot and test it with a limited group of real users.
You scale, iterate, or stop, based on thresholds we agreed on before the pilot began.
We add security, evaluation, monitoring, integrations, and named owners.
We document the system, train your team, and expand to more users or workflows once the results hold.
The maturity assessment is free, and the AI Compass Sprint and the 30-day pilot each have a fixed fee. We quote implementation after the pilot, once the scope is clear. Budget for software, model usage, and your team’s time on top of consulting.
The sprint and the pilot take about two months combined.
Typical stages:
1. Free AI maturity assessment: a quick baseline before you commit budget (1 week)
2. AI Compass Sprint: 3–4 weeks
3. AI Pilot: 30 days
4. Integration and development: from one month, depending on your data, integrations, and security requirements
Yes. Most mid-market data sits across CRM, ERP, and spreadsheets. We find what your first use case needs and fix only that. For a mid-sized lender, we unified ad, CRM, and finance data before adding AI analytics, and the team found its best-performing campaigns 40% faster.
Often both, in stages. Consultants make sense when you need expertise faster than you can hire. An internal team makes sense once AI work is steady and predictable. In between, we set up architecture and governance while your people take ownership.
Both. Strategy and engineering sit in one team, so the people who plan your roadmap also build the pilot and take it to production.
No. We don’t sell a platform, so we recommend the stack that fits your use case, systems, and budget. Every engagement ends with documentation, training, and a supported handoff, so your team can run the system without us.
Against a baseline set before work starts. Each project gets one primary KPI, a few guardrail metrics, and a full cost view: consulting, software, model usage, infrastructure, and maintenance. We keep verified savings separate from forecast revenue.
When an existing SaaS tool already solves the problem, no one can own the project internally, or the benefit won’t cover deployment and upkeep. If the assessment shows that, we’ll say so. We’ve turned clients away for exactly this reason.
Look for proof from companies of your size and a team that has taken pilots to production. Ask when they would advise against AI and who will do the work after you sign. Then compare quotes on the same scope.
Infobip 
Glia 
LivePerson 
TrapTap 
Bansk Beauty 
Alpha Guilty 
Kittch 
Speech Pathology Group 
Post 
Electronic Arts (EA Chillingo) 
Tom Ford 
Chatfuel 
MyHome 
GTO LAB 
Smart Marketer 
Handbid 
QuoteToMe 
Study Space 
YouVersion 
CNSI (Acentra Health) 
Luxury Escapes 
Facility Health 
HouseTab 