AI consulting services from people who ship
Most AI consulting ends in a slide deck. Ours ends in working software, because the consultants are the engineers who build it. We are an AI consulting company that has scoped, built, and shipped 50+ AI projects, and we will tell you when not to build, which model to use, and what it will really cost before you spend the budget.
Why AI Consulting Has a Bad Name
The failure modes are predictable, and most start before any code is written:
Strategy Decks Nobody Can Build
Consultants who have never deployed a model produce roadmaps their own firm cannot deliver. The deck is impressive; the project dies in engineering.
Discovery That Never Ends
Months of workshops and interviews billed by the hour, producing requirements documents instead of working software.
Nobody Says No
Every use case gets a green light because saying no ends the engagement. Most failed AI projects should have been stopped at scoping.
Technology Picked Before the Problem
Vendors recommend whatever they resell. The right answer is sometimes RAG, sometimes fine-tuning, sometimes a SQL query and no AI at all.
What Our AI Consulting Covers
Four engagements we run, each scoped to end in a decision or a build:
AI Strategy and Roadmap
From Use-Case List to Build Order
- Score your use cases by payback, data readiness, and risk
- Sequence a roadmap your team can actually deliver
- Define accuracy targets and success metrics before any build
- Grounded in patterns from 50+ shipped projects
Build vs Buy vs Partner
The Decision Before the Budget
- Evaluate off-the-shelf tools against a custom build honestly
- Total cost modeling, licensing versus ownership
- Vendor evaluation without resale incentives
- We recommend buying when buying is right
Generative AI Consulting
LLMs Applied Where They Pay
- Model selection across GPT-4, Claude, Llama, and Mistral
- RAG versus fine-tuning versus prompt engineering, decided on your data
- Cost modeling and optimization for LLM workloads
- Security, privacy, and compliance review for generative AI use
AI Project Rescue
When the First Attempt Stalled
- Audit of a stalled or underperforming AI project
- Root-cause findings on accuracy, data, and architecture
- A concrete fix-or-stop recommendation with cost to finish
- Re-architecture and delivery by the same team that audited
THE EDGEFIRM DIFFERENCE
Unlike strategy consultancies:
- • The consultants are the engineers
- • Recommendations come with a fixed build price
- • We say no to use cases that will fail
Unlike vendor consultants:
- • No products to resell
- • Model and tool choices on merit
- • Buy recommendations when buying wins
Unlike hourly advisors:
- • Fixed-scope engagements with deliverables
- • Weeks, not quarters
- • Every engagement ends in a decision
What We Evaluate and Recommend Across
Foundation Models
- GPT-4 & GPT-4 Turbo
- Claude 3.5 Sonnet
- Llama 3.1 & Mistral
- Azure OpenAI
- AWS Bedrock
Architectures
- RAG systems
- Fine-tuning
- AI agents & MCP
- Small language models
- Hybrid search
Data & Pipelines
- PostgreSQL & vector DBs
- dbt & ETL pipelines
- Data readiness audits
- Governance & lineage
- Warehouse integration
Delivery & Operations
- Cloud cost modeling
- Evaluation frameworks
- Security & compliance review
- Docker/Kubernetes
- AWS/GCP/Azure
Consulting Engagements We Run
AI Strategy and Roadmap
Decide What to Build First
Challenges
- • A long list of AI ideas and no way to rank them
- • Board pressure to do AI without a clear payback case
- • Data readiness unknown until projects are already funded
Our Solutions
- • Use-case scoring by payback, data readiness, and risk
- • Data audit before commitments, not after
- • A sequenced roadmap with cost and accuracy targets per phase
Typical Results
- • A build order your team can defend to the board
- • Dead-end use cases stopped before they consume budget
- • Each phase scoped with measurable targets
Illustrative outcomes from comparable deployments. Actual results depend on your data, scope, and use case.
How a Consulting Engagement Runs
Frame the Question
- Define the decision the engagement must produce
- Collect the use cases, vendors, or stalled project on the table
- Identify the data and systems involved
- Agree the evaluation criteria up front
Deliverable: Engagement charter with the decision to be made
Evidence Gathering
- Audit data readiness against the use cases
- Hands-on testing of models or tools against your real data
- Interviews with the teams who will live with the result
- Cost modeling across the candidate paths
Deliverable: Findings grounded in your data, not generic benchmarks
Recommendation
- A written recommendation with the evidence behind it
- Cost, timeline, and accuracy targets for the recommended path
- Explicit no-go calls on use cases that will fail
- Working session with your stakeholders
Deliverable: A decision your team can act on immediately
Delivery, If You Want It
- The same team that consulted can build the recommendation
- Fixed-price build quote attached to the recommendation
- No handoff loss between strategy and engineering
- Or take the recommendation and build elsewhere; you own it
Deliverable: Optional fixed-price build proposal
Transparent Pricing for AI Consulting
Typical Investment Range
$20,000 - $75,000
Fixed-scope engagements, typically 3 to 6 weeks
Factors that affect pricing:
Decision Scope
One build-vs-buy call versus a full portfolio roadmap
Data Audit Depth
How many systems and datasets need readiness assessment
Hands-On Evaluation
Whether tools and models are tested against your real data
Stakeholder Breadth
How many teams and decision-makers the engagement spans
What's Included:
Common Questions About AI Consulting Services
Four engagement types: AI strategy and roadmap (which use cases, in what order, at what cost), build versus buy evaluation, generative AI adoption (model selection, RAG versus fine-tuning, security review), and rescue of stalled AI projects. Every engagement is fixed-scope and ends in a written recommendation with evidence.
The consultants are the engineers. The people who assess your use cases are the same people who shipped 50+ AI projects, so every recommendation comes with a real cost, a real timeline, and optionally a fixed-price build quote. We have no products to resell, and we put no-go calls in writing when a use case will fail.
Yes, and it happens regularly. Most failed AI projects should have been stopped at scoping: the data was not ready, the accuracy ceiling was too low, or a simpler tool already did the job. A no-build recommendation costs you a few weeks of consulting instead of a year of engineering.
Yes. Generative AI consulting covers model selection across GPT-4, Claude, Llama, and Mistral, the RAG versus fine-tuning versus prompt engineering decision made against your actual data, LLM cost modeling and optimization, and security and compliance review before anything customer-facing ships.
Engagements run $20,000 to $75,000 fixed price and take 3 to 6 weeks depending on scope. If the recommendation is to build and you want us to build it, the consulting findings carry straight into a fixed-price delivery proposal with no re-discovery.
Where Consulting Leads:
Custom LLM Applications
When the recommendation is a custom build, this is how we deliver it.
Learn MoreAI Integration Services
When the gap is connecting AI to your existing systems, not strategy.
Learn MoreGuide: Build vs Buy vs Partner
Our public decision framework, free before you ever talk to us.
Learn MoreGuide: Why Enterprise AI Projects Fail
The failure modes we screen for in every scoping engagement.
Learn MoreReady to Transform Your Business with AI Solutions?
Schedule a free strategy call to discuss your project and get a custom AI implementation roadmap.
Or email us directly at hello@edgefirm.io. We typically respond within 2 hours during business days.