AI Consulting Services
Figure out where AI belongs in your product. Then build it with the same team.
Most AI consulting ends with a strategy document you can’t execute. Ours ends with a clear plan, scoped deliverables, and the option to build it with the engineers who designed it. No handoff. No gap between advice and reality.
The problem with most AI consulting
There is a pattern in how AI consulting typically goes. A firm conducts stakeholder interviews, benchmarks against competitors, and delivers a roadmap. The roadmap is thorough. It has a phased implementation timeline, a technology recommendation section, and an executive summary.
Then what?
The firm leaves. Your engineers look at the document and ask who is supposed to build this. The recommendations describe capabilities you don’t have yet, infrastructure that doesn’t exist, and decisions that were made without seeing your actual codebase or your data.
This happens because most AI consulting is done by strategists who don’t build, or by large firms where the team that sells the engagement is different from the team that executes it. The advice is disconnected from the reality of your product and your technical situation.
GTC is a product engineering team. Our consulting is done by engineers who have built real AI features into real products. The advice is grounded in what can actually be built, what it will cost, and what it will take.
And when the consulting is done, you can choose to build it with us. The same team. No translation required.
What our AI consulting covers
Six ways we help you decide and plan.
AI Strategy and Roadmap
We work with you to define where AI fits in your product strategy, which initiatives to prioritize, and how to phase the work over a realistic timeline. This isn’t a generic AI maturity framework. It’s a specific plan for your product, your data, and your resources — with clear decisions on what to build first and why.
AI Readiness Assessment
Before building anything, you need an honest picture of where you are. We assess your product architecture, your data sources, and your current workflows to understand what’s technically ready for AI, what needs foundational work first, and where the real blockers are. The output is a clear readiness report, not a sales pitch for more work.
AI Use Case Discovery and Prioritization
Not every part of your product benefits from AI. Some problems are better solved with better UX, better data structure, or simpler automation. We work through your product and workflows to identify where AI genuinely adds value for your users and your business, then rank opportunities by impact and feasibility. You’ll know what to build first and what to skip.
AI Architecture and Technical Planning
Once you know what to build, you need to know how to build it properly. We design the technical approach: which AI providers and models fit the use case, how to structure the integration, how to manage data flow and context, how to handle costs and failure states, and what the full technical scope looks like before code is written.
Build vs. Buy Assessment
Should you integrate an existing AI API, use an off-the-shelf tool, or build a custom pipeline? The right answer depends on your use case, your data, your compliance requirements, and your budget. We analyze the options and give you a clear recommendation with the reasoning — with no vendor relationships that bias our view.
AI Product Roadmap
If you’re planning AI features over a three to six month horizon, sequencing matters. We help you define what to build first, what needs foundational work before it’s feasible, how to phase delivery so you’re getting user value at each stage, and what the resource and timeline picture looks like across the full roadmap.
Not sure which consulting service fits your situation?
Tell us where you are and what you’re trying to figure out. We’ll recommend the right starting point.
Engagement models
Defined scope, clear output, fixed timeline.
We don’t run open-ended consulting retainers. Every engagement has a defined scope, a clear output, and a fixed timeline. How you engage depends on where you are.
AI Assessment
You have a product and you’re not sure where AI adds real value or whether you’re ready for it. We assess your current state and deliver a clear findings report. Useful as a standalone engagement or as the first step before a larger roadmap.
AI Strategy and Roadmap
You know AI belongs in your product but need a clear plan: what to build, in what order, and what it will take. We produce a specific roadmap with phased priorities, technical recommendations, and realistic timelines. This includes a readiness assessment as part of the process.
AI Architecture and Technical Planning
You’ve decided what to build and need the technical design: which approach, which providers, how the integration fits into your existing architecture, and what the build scope actually looks like. This is the bridge between strategy and engineering.
Consulting plus Build
In many engagements, the consulting and the build happen with the same team. We assess, plan, and then execute. This is faster, cleaner, and removes the gap between what was designed and what gets built.
Step 1: Initial conversation
A senior product engineer understands your product, your goals, and where you are right now. We determine what kind of engagement fits your situation, or whether you’re already clear enough to skip straight to building.
Step 2: Assessment
We conduct a structured review of your product, data, and technical context. Depending on scope, this includes codebase review, data source mapping, workflow analysis, and interviews with your product and engineering teams. We work through your actual situation, not a generic AI maturity framework.
Step 3: Use case identification and prioritization
We identify the AI opportunities that are worth pursuing in your specific context, score them by impact and feasibility, and agree on the priorities. We also tell you which ideas aren’t worth pursuing and why.
Step 4: Architecture and roadmap design
For each priority use case, we design the technical approach and produce a phased roadmap: what gets built, how it gets built, in what order, and what it will realistically take. This is a working document, not a slide deck.
Step 5: Findings delivery and decision
We walk through the output with your team, answer questions, and adjust based on your input. At this point, you have a clear plan you can act on. You take it and execute with your own team, or you work with us to build what we’ve scoped. That choice is yours.
Step 6: Post-engagement support (optional)
If questions come up during your build, or you want us to review work as it progresses, we’re available. We don’t disappear after delivery.
The technology we advise on
Advice based on fit, not vendor relationships.
We advise based on what fits your use case, not on vendor relationships. The AI ecosystem we work across:
Language model providers
OpenAI (GPT-4 and newer), Anthropic Claude, Google Gemini, and open-source models (Llama, Mistral, and others) for use cases where data privacy or cost constraints make them the right choice.
Integration and retrieval infrastructure
RAG architectures, vector databases (Pinecone, Weaviate, pgvector), document stores, and API integration layers. We design retrieval pipelines that connect AI to your actual data.
Application and platform stack
React, Angular, Node, Java, React Native, iOS, and Android. We advise on how AI fits into your existing application architecture, not in the abstract.
Cloud platforms
AWS, Google Cloud, and Azure. Infrastructure and deployment recommendations aligned with your existing environment.
AI tools and frameworks
LangChain, LlamaIndex, and other orchestration frameworks where relevant. We evaluate these against your specific use case rather than defaulting to the most popular option.
Industries where we consult and build
Domain context before we advise on anything.
Real estate and proptech.
AI strategy for property platforms covering intelligent search, document extraction for transaction workflows, lead routing, and investor-facing tools. We understand the data complexity and compliance considerations in this space before we advise on anything.
Healthcare.
AI readiness assessments for clinical workflow tools, use case prioritization for patient-facing AI features, and architecture planning for systems that must handle sensitive data carefully. Compliance is a starting constraint, not an afterthought.
Education platforms.
Use case discovery and feasibility assessment for learning platforms considering AI-driven content, feedback, and search features. We’ve worked with early-stage edtech products and institutional platforms.
Enterprise SaaS.
AI product roadmapping for software companies adding intelligence to existing platforms, build vs. buy assessments for AI features, and technical architecture for AI copilots and internal knowledge tools.
Marketplace platforms.
Use case discovery and feasibility for AI-powered search, matching, categorization, and recommendation in two-sided and multi-sided platforms.
Who we work with
Teams with a real product and a specific decision to make.
Growth-stage founders
who know AI belongs in their product but aren’t sure where to start or whether their current setup is ready for it.
CTOs at mid-market companies
who need an independent technical view on AI direction before committing budget to a build. They’ve seen AI projects fail elsewhere and want the honest assessment first.
Product leaders
who are planning AI features and need to understand what’s feasible, what it will take, and in what order to build it.
Engineering leads
who have been handed an AI mandate from leadership and need a credible technical plan to execute it.
We work best with teams that have a real product and a specific decision to make. We’re not the right fit for teams that are still exploring whether they need AI at all — that’s a business strategy question, not a product engineering question.
Why consulting from engineers is different
We've lived with the consequences of the advice we give.
Most AI consulting is done by strategists who advise but don’t build. The advice is produced without seeing your codebase, without knowing how your data is structured, and without the accumulated judgment of having built similar things before and seen what breaks in production.
Our consulting is done by engineers who build AI features into real products. When we say an approach is feasible, it’s because we’ve built something like it. When we say an approach has risks, it’s because we’ve seen those risks in production. When we design an architecture, it’s designed to be actually built, not to look good in a presentation.
The practical difference shows up in what we deliver: specific technical decisions, realistic timelines, honest cost estimates, and a clear answer on what won’t work and why. These are things that only come from having to live with the consequences of the advice you give.
What we won't do in a consulting engagement
We won’t deliver a 60-slide AI transformation roadmap with generic recommendations that could apply to any company in your industry.
We don’t design AI operating models, Centers of Excellence, or enterprise change management programs. We don’t build data governance frameworks or run AI ethics audits.
We also won’t recommend building custom ML models or AI infrastructure if your use case doesn’t require it. Most companies that come to us for consulting discover they can get what they need with existing AI providers, well-engineered integration, and a clear product architecture. We’ll tell you that if it’s true.
What we do is specific: help product companies and technical teams figure out where AI fits, what to build, how to build it, and whether to build it with us or with their own team.
"We needed AI search built into our platform without rebuilding the whole product. GTC designed the integration cleanly, it shipped on time, and it actually improved how users found things. That was the measure that mattered."
"They were honest from the start about what AI would and wouldn't solve for our specific product. That scoped the project correctly from day one. The integration worked in production on the first try."
FAQ
Questions teams ask before engaging.
Two things. First, our consulting is done by engineers who build AI features, not strategists who advise on them. The advice is grounded in what can actually be built, what it costs, and what the real technical risks are. Second, when consulting ends, you can choose to build with the same team. There’s no handoff to a separate engineering organization and no gap between the recommendation and the execution.
A working document your team can act on. Depending on the engagement type, that includes: a readiness assessment with clear findings, a prioritized use case list with reasoning, technical architecture recommendations, a phased implementation roadmap, a build vs. buy recommendation, and honest cost and timeline estimates. We don’t produce slide decks for executive presentations. We produce technical recommendations designed to be executed.
An active one. We need access to the right people: usually your product lead, a senior engineer who knows the codebase, and whoever owns the data. We conduct structured working sessions, not just interviews. Your team’s context is essential to producing recommendations that actually fit your situation. We don’t work in isolation and then present findings at the end.
We agree on success metrics before we start, not after. Depending on your goals, those might be: confidence in a specific build decision, a prioritized use case list that was previously unclear, a technical architecture that unblocked your engineering team, or a budget and timeline estimate that let you secure internal approval. We also define the downstream metrics for the AI features we recommend, so you know what success looks like when the build is done.
A focused AI readiness assessment or use case prioritization typically takes one to two weeks. A full strategy and roadmap engagement for a more complex product typically runs two to four weeks. An architecture and technical planning engagement is one to three weeks. We scope every engagement clearly before starting so timelines aren’t moving targets.
Probably not as a separate engagement. If your requirements are clear and you have a specific AI feature in mind, the scoping work happens as part of the project kickoff. We’ll have that conversation in the initial 30-minute call and tell you which situation you’re in.
That failure happens for a specific reason: the people who produce the strategy aren’t the people who would build it. The recommendation is disconnected from the reality of your codebase, your data, and your team’s capability. Our consulting doesn’t have that gap. It’s done by engineers who build what they recommend, so the output is designed to be executed, not presented.
We operate on a need-to-know basis. We access what’s necessary to produce accurate recommendations and nothing more. For healthcare and other regulated products, we bring compliance awareness into the assessment from the beginning. If your data situation has specific constraints, we factor those into the architecture recommendations rather than treating them as an afterthought.
Yes. In many engagements, our role is to produce the plan that your team then executes. In others, we work alongside your team, handling the AI integration work while your engineers continue on the core product. We communicate clearly, document thoroughly, and design for long-term ownership by your team.
You leave with a clear, actionable output. You can execute it with your own team, or work with us to build what was scoped. If you build with your team and questions come up, we’re available for follow-up. If you build with us, the consulting output becomes the foundation for the build engagement. Either way, you’re not left with a document and no support.
Not sure where to start with AI? Start here.
Not sure where to start with AI? Start here.
If you’re trying to figure out where AI belongs in your product, whether a specific AI idea is feasible, or how to sequence the work, tell us about it. We’ll walk through your situation and tell you what makes sense.
Thirty minutes. A senior engineer. An honest answer.
No pitch. If consulting isn’t what you need, we’ll tell you that too.