Generative AI Consulting
Generative AI Consulting: Where It Pays, and Where It Does Not
Generative AI consulting in a short engagement that maps the opportunities against your data, your policies and your cost tolerance, then names the one or two worth piloting first, with the numbers attached.
Talk to Our Generative AI Team
A look at what the numbers say about the team behind your build.
- 45+
- Engineers
- 50+
- Projects Deployed
- 10+
- Industries Worked In
- 2
- Development Facilities
- 10+
- Years of Experience
- 100+
- Happy Customers
- 24/7
- Support Availability
- 95%
- Client Retention
01 Trusted by
Teams whose GenAI plans we shaped
From venture-backed startups to enterprise functions, these are the teams whose generative AI programmes we helped scope.
02 Capabilities
Generative AI Consulting Services
Generative AI consulting covers opportunity mapping, policy review, pilot design and cost modelling. All of it happens before anyone writes a prompt, because the expensive mistakes in generative AI are made in the choosing rather than in the building of any particular feature.
What is fine-tuning?-
Opportunity Mapping
Every candidate use case captured and sized on value, effort and data dependency, with the ones we would not build named explicitly.
What is fine-tuning? -
Policy & Risk Review
What your sector, contracts and customers permit: disclosure, retention, residency and human-review obligations, translated into build constraints.
What is fine-tuning? -
Pilot Design
One use case scoped small enough to prove in weeks and structured so its result actually settles the question it was meant to answer.
What is fine-tuning? -
Cost & Unit Economics Model
Token, infrastructure and review costs per transaction, so a feature that works technically is checked for whether it works commercially.
What is fine-tuning? -
Build vs Buy Assessment
Where a tool already does this well enough, where it does not, and what three years of licence costs look like against building.
What is fine-tuning? -
Team Readiness Review
Whether your team can run what gets built, and whether training, hiring or an embedded team is the cheaper answer.
What is fine-tuning?
03 Our process
How a Generative AI Consulting Engagement Runs
A discovery runs in five steps over two to four weeks. We map the opportunities, test them against your data and your policies, model the running cost of each, then recommend the one or two worth piloting first. You finish with a costed recommendation you can act on or decline.
Get Started-
Kickoff & Stakeholder Map
We agree the question the discovery has to answer, who needs to believe the answer, and what evidence would change their mind.
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Use-Case Inventory
Every candidate captured and sized on value, effort and data dependency, including the ones we will recommend against.
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Data & Systems Review
Where the data lives, how clean it is, and how AI would actually reach your systems: identity, latency, failure behaviour.
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Prioritisation Workshop
We rank the inventory with your stakeholders in the room, so the sequence is agreed rather than delivered.
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Roadmap & Costing
A phased plan with costs, risks and assumptions attached, and a recommended first phase small enough to prove.
04 Industries
GenAI Strategy for Regulated Industries
Recommendations that account for the disclosure, retention and residency rules each sector runs on.
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ITSM
Service-desk copilots that triage tickets and deflect L1/L2 volume, built and maintained by dedicated engineers in your existing tooling.
9+ Projects -
Legal
Contract and trust document review with retrieval over your own precedent library, cutting document review time by 60%.
6+ Projects -
Healthcare
Clinical documentation, intake, and patient records under HIPAA-compliant, auditable data flows, reducing documentation time by 40%.
10+ Projects -
Real Estate
Listing intelligence, valuation support, and agent workflows where search, updates, and coordination run continuously.
7+ Projects -
Construction
Estimating and field reporting connected to systems crews already use, delivered by dedicated teams who ramp into your stack.
8+ Projects -
Logistics
Dispatch, tracking, and exception handling where timing and visibility drive the operating decisions of the day.
6+ Projects
05 Case studies
Programmes we have shaped
Plans that turned into products still running today.
Change Management
AI-Powered Enterprise Change Management for Large-Scale Organizations
Enterprise change management software letting organisations execute, monitor and sustain change faster, with the progress of every initiative visible in one place.
Construction Management
Real-Time Construction Coordination, From HQ to Field
A construction coordination platform for multi site work, replacing spreadsheets and scattered email with tasks, schedules and on site progress synced in real time.
Legal & Estate Planning
Building a Secure, Multi-Portal Legal Platform
A digital estate planning platform running end to end across three portals, simplifying legal documentation and improving transparency for every party to a trust.
AI Based Real Estate Property
AI-Driven Property Insights Platform
Landwise NWA transforms commercial real estate workflows by providing AI-powered insights and data integration for faster decisions.
AI Automation
AI-Powered Content Automation Platform
An AI content engine that turns sales and strategy calls into ready-to-post content, matched to the brand's own voice and scheduled straight to LinkedIn.
06 Client review
Support did not stop at launch.
Weekly demos during the build, documented decisions, and a support arrangement that has now run for two years past release.
Trustpilot
07 Tech stack
The options we benchmark
The models, frameworks and tooling we assess your choices against.
- OpenAI
- Claude
- Gemini
- Llama 3
- Mistral
- Falcon
- LangChain
- LlamaIndex
- Haystack
- DSPy
- LoRA
- PEFT
- Axolotl
- Unsloth
- Pinecone
- Weaviate
- pgvector
- Qdrant
- Chroma
- OpenAI API
- Anthropic API
- Vertex AI
- Bedrock
- LangSmith
- Langfuse
- Arize
- Helicone
- Guardrails AI
- NeMo Guardrails
- Presidio
- Streamlit
- Chainlit
- Next.js
- Vercel AI SDK
08 Challenges
Why GenAI pilots stall
Generative AI pilots stall when the use case was chosen by enthusiasm, when policy said no after the build, or when nobody modelled what the tokens would cost at real volume. A discovery catches all three while changing direction is still cheap.
Everyone has an idea, nobody has a number
A pilot that could not have failed
Unit economics that only work at demo scale
Policy found after the build
Buying what you should build, or the reverse
No plan for who runs it
09 Advantages
Why Teams Bring Us In for Generative AI Consulting
Teams bring us in first because engineers do the consulting, so the estimates come from the people who would build the thing. That also means we can tell you a use case is not worth it, which is harder advice to give once a delivery contract is signed.
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Engineers Doing the Consulting
The people writing your roadmap have shipped the systems it describes, so the estimates come from delivery experience rather than from a framework.
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Two to Four Weeks, Not Two Quarters
A discovery is scoped to end in a decision. You get a report you can act on or decline, without an open-ended engagement attached.
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We Will Tell You Not To Build
The audit ranks use cases honestly, including the ones where an existing tool or a simpler process wins. That is what makes the rest of the ranking worth trusting.
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Costs and Risks Attached
Every phase carries an estimate and the assumptions behind it, so the roadmap survives a finance review rather than dying in one.
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Unit Economics, Not Just Feasibility
We model cost per transaction at real volume, because "it works" and "it pays" are different questions and only one of them gets asked.
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Policy Translated Into Constraints
Disclosure, retention and residency rules become build requirements in the report rather than surprises at the security review.
10 More services
What else we build
The rest of what we build, and the teams who build it.
- Business Process Automation We automate the repeatable parts of a process, from document intake to decisions and handoffs, with an exception path designed before the happy path.
- AI Consulting A short discovery that identifies high-impact use cases, reviews your data and systems, and returns a phased roadmap with costs and risks attached.
- AI Integration We wire AI into the systems you already run, with identity, data, latency and failure behaviour designed before anything ships.
- Chatbot Development Assistants that answer from your own content, hand off cleanly to a person, and log every conversation for review.
- Generative AI Development We design and integrate large language model features into your product, with retrieval, evaluation harnesses, and prompt governance in place before launch.
- AI Agent Development Assistants and autonomous agents that work inside your systems, handling support, intake, and internal workflows with human handoff where it matters.
- AI Copilot Development Copilots built into your product, so the assistant works where your users already are rather than in a separate window.
- Machine Learning Development Forecasting, classification, and computer vision models trained on your data, deployed with monitoring so accuracy is measured rather than assumed.
- Machine Learning Consulting An assessment of whether the signal you need exists in your data, and what a model would actually be worth.
11 FAQ
Frequently asked questions
The things teams ask before starting with us.
Generative AI consulting is worth it when you have several candidate use cases and no agreed way to choose between them. The engagement tests each against your data, your policies and your cost tolerance, then names the one or two worth piloting, with running costs modelled at real volume.
AI Consulting covers the whole AI surface, machine learning, automation, integration. This one is narrower and deeper on generative AI specifically: prompt and model economics, disclosure and retention policy, hallucination risk, and the pilot design that settles whether to go further. Teams who already know they want GenAI start here.
A ranked opportunity map, a policy and risk summary, a unit-economics model, and a designed pilot with success criteria agreed in advance. Enough to fund a decision, and written so another supplier could execute it.
We start with a short discovery call to clarify goals, constraints, and success metrics. Then we identify high-impact AI use cases, review your data and systems, and propose a phased roadmap with costs and risks attached to each phase.
Discovery runs 2 to 4 weeks, an MVP typically lands in 8 to 16 weeks of weekly-demo sprints, and operate is an ongoing arrangement with monitoring and support after launch.
Fixed-scope work is priced per milestone, dedicated teams are billed monthly per seat, and staff augmentation is weekly or monthly. You pick the model that fits and can switch between phases. There is a fuller breakdown of what moves the number on our AI development cost page.
You do. The full repository, documentation, and deployment transfer to you at every milestone, with no lock-in to us.
NDAs from day one, scoped access, and data residency decided at the architecture stage. We work within HIPAA, SOC 2, and similar requirements where they apply.
We monitor accuracy, cost, and uptime, run regression checks as models drift, and keep a defined escalation path with support so the system stays healthy after release.
12 Book a Call
Ready to book a discovery?
Tell us where you are and what has to be true for this to work. We come back with an approach, a timeline and a number.
- 2451 West Grapevine Mills Circle, Grapevine, TX 76051 · USA
- hello@kodertal.com
- Reply within 1 business day, 24/7 support once live