AI consulting services from the team that builds the systems
Our AI consulting services end in a costed roadmap and a build plan your board can approve. The same ScalaCode team that writes the plan can build it, and the plan is yours to take to any vendor.
AI systems running in production
clients served since 2012
shipping software since 2012
on Clutch, from 33 verified reviews
countries with live clients
9001:2015 certified delivery process
Trusted by teams at
Clutch 4.8/5Verified client reviews
GoodFirmsReviewed software partner
ISO 9001:2015Certified delivery process
GDPR compliantData handled under EU rules
What AI consulting services deliver
A consulting engagement earns its fee when it ends in a decision your team can fund, with a scope, a cost and a date attached.
- What do AI consulting services include?
- AI consulting services help a company decide where artificial intelligence pays off and how to build it. The work covers use-case discovery, data and systems review, model and vendor choice, governance, and a costed roadmap that names the first build, its success metric and its timeline.
Related: AI strategy sets the direction. RAG development and agentic AI are the builds most roadmaps start with.
Which AI consulting engagement fits where you are
Most companies start with the discovery sprint. Teams that already know their use case can go straight to a proof of concept.
| Discovery sprint | Proof of concept | Production build | |
|---|---|---|---|
| Question it answers | Where AI pays off for us, and in what order | Whether this use case works on our data | How we run it for every user, every day |
| What you get | Scored use cases, a data review, a costed roadmap and a build plan | A working system on your data, scored against an evaluation set | A production system inside your tools, with monitoring and handover |
| Pick it when | You have ideas and budget but no agreed priority | One use case is agreed and needs proof before funding | The proof of concept met its metric |
| Typical timeline | 1 to 2 weeks | 3 to 4 weeks | Set after the proof of concept |
| Where it falls short | Produces a plan and no running software | Covers one use case at limited scale | Needs a clear scope, so it follows one of the first two |
When the first use case is a knowledge assistant over your documents, the build moves into RAG development.
Enterprise AI consulting engagements
Enterprise AI consulting from engineers who ship production systems, so every recommendation comes with a build estimate.
Use-case discovery and scoring
We map candidate use cases with your teams and score each on business value, data readiness, risk and build effort.
Data readiness review
We check the documents, records and systems each use case depends on, and list the fixes needed before any model work.
AI roadmap and business case
We write a sequenced plan with scope, cost, timeline and the metric each step must move, ready for budget approval.
Build, buy or integrate
For each use case we compare an off-the-shelf tool, an integration into your current systems and a custom build.
See AI integrationModel and vendor selection
We test OpenAI, Anthropic Claude, Google Gemini or open-weight Llama and Mistral on your own questions, and check where a fine-tuned small language model would do the job for less.
AI governance and risk
We map controls to GDPR, HIPAA and the EU AI Act, with access control, audit logs and human review where output carries risk. Sector rules are covered for healthcare AI and fintech AI.
Proof of concept design
We write the scope, the evaluation set and the pass criteria, so the proof of concept ends in a clear go or stop.
AI transformation consulting
We plan the teams and processes around the system, including who reviews output and who owns the metric.
Agent and automation planning
We work out which workflows suit an AI agent that acts in your systems, and which need a person to approve each step.
See agentic AI developmentHave a list of AI ideas and no agreed first project
Bring the list to a 30-minute call. We will tell you which one we would start with, what data it needs and roughly what it costs.
AI transformation consulting decisions we settle early
Four decisions shape the cost and risk of an AI programme more than the choice of model.
One workflow, one metric
Each use case is tied to one workflow and one number a named person owns, such as handling time per ticket or hours per report.
Retrieval before fine-tuning
When the problem is missing knowledge, we recommend RAG over your documents. Fine-tuning comes in only for fixed formats, tone or cost targets.
Integrate before you rebuild
AI usually reaches users faster through the CRM, ERP or support desk they already use, connected through existing APIs.
Human review where it counts
Output that affects money, health or legal standing goes to a person for approval, and every decision is logged.
Planwise: AI electrical takeoff from residential plans
OCR and computer vision read each plan's own legend and count every lighting, power, fan and data point, with bounding boxes electricians can check and correct.
Takeoff cycle time, per the client
How an AI consulting engagement runs
The discovery sprint takes 1 to 2 weeks. If you go ahead, a proof of concept takes 3 to 4 weeks, and the production schedule is agreed in writing once it passes.
One team, 14 years of shipping
projects shipped since 2012
AI systems running in production
clients across 45+ countries
years building software, since 2012
AI governance written into the roadmap
Every plan we write names the controls each use case needs before it reaches users, mapped to GDPR, HIPAA and the EU AI Act where they apply.
Access control
The AI sees only the data each user is allowed to see in the source system.
Audit logs
Prompts, responses, model versions and approvals are logged for review.
Human review
High-stakes output waits for a person to approve it before anything changes.
Sensitive data redaction
Personal and confidential fields are masked before any model sees them.
Models and platforms we assess for your roadmap
We recommend per use case, on accuracy against your questions, data residency, latency and cost.
Where AI consulting pays off first
Industries with high document volume, strict rules on data, or repetitive decisions that follow written policy.
Financial services
- Document review and KYC support
- Policy Q&A for staff
Healthcare
- Clinical documentation drafts
- HIPAA-aware data controls
Retail and eCommerce
- Product content at scale
- Support ticket triage
Logistics
- Shipping document extraction
- Exception triage
Manufacturing
- Maintenance knowledge search
- Quality report summaries
SaaS platforms
- In-app assistants
- Per-tenant data controls
Legal and compliance
- Contract clause review
- Regulatory change summaries
Professional services
- Proposal drafting
- Knowledge base search
AI consulting cost
The discovery sprint is free. You own the roadmap either way.
AI discovery sprint
1 to 2 weeks and free, ending in a written proposal with a fixed quote. Scored use cases, a data review, governance controls and a costed roadmap.
Best for teams that need an agreed first project and a budget.
AI proof of concept
From $4,999, 3 to 4 weeks. One use case built on your data and scored against an evaluation set with agreed pass criteria.
Best for one agreed use case that needs proof before funding.
Dedicated AI team
Engineers who build the roadmap inside your process and tools, from $1,799 a month per engineer.
Best for a roadmap with several builds in sequence.
Rated 4.8/5 by the clients we build for
Verified reviews from Clutch and GoodFirms, and clients on camera.
33 verified reviews on Clutch
Clutch review★★★★★“What stood out to us as most impressive and unique about ScalaCode was their ability to humanize complex AI.”
Clutch review★★★★★“They managed to balance technical skill with an understanding of the marketing world.”
Clutch review★★★★★“What impressed us most was how quickly ScalaCode understood the operational challenges we were trying to solve.”
Clutch review★★★★★“What sets ScalaCode apart is their technical AI expertise and an understanding of a client's organizational ethos.”
GoodFirms review★★★★★“They built a high-performance, AI-powered platform that transformed video engagement for learning.”
Clutch review★★★★★“Their team often proposed innovative features we hadn't considered.”
Clutch review★★★★★“They didn't just build an algorithm; they built a tool that respects the nuances of Indigenous reconciliation.”
AI consulting services FAQs
We will answer anything not covered here on the call.
What do we get at the end of AI consulting?
You get a written roadmap with scored use cases, a data readiness review, the governance controls each use case needs, and a scoped first build with its cost, timeline and success metric. The documents are yours, and you can take the plan to any vendor or build it in-house.
How much do AI consulting services cost?
Our AI consulting services start with a free discovery sprint. A proof of concept on one use case starts at $4,999. A production build is quoted from the roadmap once the scope is clear.
How long does AI consulting take?
The discovery sprint takes 1 to 2 weeks. A proof of concept takes 3 to 4 weeks after that. The production timeline depends on integrations and compliance scope, so we fix it in writing once the proof of concept passes, and AI coding agents help our engineers ship the build faster.
Do you also build what you recommend?
Yes. The same team that writes the plan can build it, so every estimate in the roadmap comes from engineers who ship production AI systems. You can also take the plan to another vendor at no cost. Our AI development services pick up the build, following the same delivery process we use on every project.
Do we need clean data before we start?
No. Reviewing your data is part of the discovery sprint. We check the documents and records each use case needs, list what has to be fixed, and rank use cases partly on how ready their data is, so the first build can start on data you already have.
How do you pick which AI use case to start with?
We score each candidate on business value, data readiness, risk and build effort, then pick the one that moves an owned metric soonest with the least risk. The first project is usually a narrow workflow with a known volume, such as ticket triage or document review, where the saving is measurable in weeks.
What does an AI consulting company look at in our existing systems?
We look at the CRMs, ERPs, support desks and internal tools the use case touches, their APIs, and the access rules that apply. Most AI features reach users through these systems via secure adapters, so the review decides whether a use case needs an integration or a new application.
How do you handle AI governance and compliance in the plan?
Each use case in the roadmap lists its controls, mapped to GDPR, HIPAA and the EU AI Act where they apply. Typical controls are access rules carried from the source system, audit logs, redaction of sensitive fields before any model sees them, and human review on high-stakes output.
One team, matched to your working hours
We schedule 4 to 6 hours of daily overlap with US time and a full working day with the UK and Europe, so reviews, demos and fixes happen during your working hours.
They understood the business side of our requirements and often brought up solutions or ideas we hadn't considered.
Find out which AI project to fund first
- 30 minutes with the ScalaCode team
- Which use case we would start with, and why
- What it would cost and how long the first build takes
“What sets ScalaCode apart is their technical AI expertise and an understanding of a client's organizational ethos.”
Laurence Longet, Director, MGEN (Clutch review)