We fix six problems with AI
DILR.ai is an AI consulting firm in London. These are the six problems businesses hire us to fix most often in 2026: code that ships no faster, rising AI bills, agents that fail in production, knowledge nobody can find, follow-up that slips, and calls nobody answers. Each one comes with its evidence.
Why has AI not made software delivery faster?
AI made coding faster but code review became the bottleneck
Developers finish more work with AI. The review queue grows faster than the output, so the business ships less often than before.
Write
+33.7%tasks completed per developer on teams with high AI adoption
Review
5xlonger median review time, with pull requests 51% larger
Ship
11.7%fewer deployments a week, and incidents per pull request up 242.7%
Why is our AI bill going up when token prices are falling?
AI bills rise because usage grows faster than prices fall
The price of a given level of model quality falls every year. Enterprise spending on model APIs still more than doubled in six months.
Enterprise spend on model APIs
- Late 2024$3.5B
- Mid 2025$8.4B
What brings the bill down
Send easy questions to a small model
Keep the large model for the few requests that need it.
Cache the prompt that repeats
Anthropic bills cached input at a tenth of its normal input price. Source: Anthropic prompt caching pricing.
Cap what an agent may spend
A hard budget per session stops one loop from becoming the month's bill.
Retrieve a paragraph instead of a document
Right-sized retrieval stops the knowledge base from inflating every prompt.
Why do AI agents work in the demo but fail in production?
Agents fail in production when nothing around the model is built
The model is rarely the problem. What is missing is the testing, the permissions and the record of what the agent did.
Same model, Terminal Bench 2.0 score
- Before the harness was rebuilt52.8%
- After the harness was rebuilt66.5%
What we build before go-live
Tests that score every change
So anyone can say whether a change made the agent better or worse.
Permissions the agent cannot exceed
It can act on what it is allowed to touch and nothing else.
A record of every run
When something goes wrong, the investigation starts from evidence.
Gartner forecasts that over 40% of agentic AI projects will be cancelled by the end of 2027, citing cost, unclear value and weak risk controls: Gartner, 25 June 2025.
How much time do employees spend looking for information?
Staff spend about 1.8 hours a day looking for information
The answer usually exists in a policy, an email or a PDF. Finding it takes close to a quarter of the working week.
What we do
Answer from your own documents and cite the source
We index the documents you already have and answer from them with the source shown, so a wrong answer traces back to a file you can fix. Dilr Voice answers live phone calls from this kind of knowledge base, and Dilr Mira does the same for clinical documents on your own hardware.
How do you stop follow-up work falling through the gaps?
Trigger the follow-up automatically when the call ends
The CRM note, the customer email and the callback task should not depend on someone remembering. A flow can run them the moment a call ends.
A real Dilr Voice flow
The call ends
That event starts the flow, at any hour.
The flow checks what happened
Here it branches on how positive the caller was.
The follow-up goes out
An email, a CRM record or a callback task, in the same minute.
Gartner forecasts 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from under 5% in 2025: Gartner, 26 August 2025.
How fast do customers expect a business to reply?
A quarter of UK home sellers want a reply within an hour
For an estate agent, a clinic or a trades business, the unanswered call is where revenue leaks. An AI receptionist answers every call at any hour.
- 1 in 4say 60 minutes is the longest acceptable wait for a reply
- 2 in 3expect a reply within four hours
- 63%would rather be contacted by phone
Which AI problem should a business fix first?
Start with the problem you can already measure
Our consulting runs in three engagements. You can start with the diagnostic alone, and stop there if the honest answer is that AI will not pay.
4 to 6 weeks
AI Placement Diagnostic
Maps where AI creates value in your business and returns a ranked roadmap, plus a list of what not to build.
How the Placement Diagnostic works6 to 10 weeks
AI Operating Model
Designs the governance, ownership and lifecycle that let an agent run safely once it is live.
How the Operating Model works12 months or more
AI Execution Office
Embeds with your team to take one placement from pilot to production and hand it to a named owner.
How the Execution Office works
What does Reddit say about AI at work?
We link the Reddit threads so you can read them yourself
We link the searches instead of quoting them. A thread cannot be checked the way a report can, so we leave the judgement to you.
Does AI actually make developers faster?
Reddit search: does ai make developers faster
Is AI code review slowing teams down?
Reddit search: ai code review bottleneck
How do teams cut their LLM API bill?
Reddit search: reduce llm api costs
Are AI agents reliable in production?
Reddit search: ai agents in production reliability
Is a RAG knowledge base worth it?
Reddit search: company knowledge base rag worth it
Do AI receptionists work for small business?
Reddit search: ai receptionist small business
What else do people ask?
Short answers to common questions about AI solutions
Short answers on what AI fixes, what it costs and where to start. Each links to the page with the detail.
- The six DILR.ai is hired to fix most often are code that is written faster without delivery getting faster, AI bills that keep rising, agents that fail in production, knowledge nobody can find, follow-up that falls between tools, and calls nobody answers. Each has a measurable cost. The Placement Diagnostic ranks which of yours is worth fixing first and which to leave alone.
- Because review became the bottleneck. Faros AI telemetry on 22,000 developers found teams with high AI adoption completed 33.7% more tasks, while their median review time was about five times longer and they deployed 11.7% less often each week. The fix is the review and verification stage, set out in full on our AI developer productivity page.
- Usage grows faster than prices fall. Epoch AI measured the price of a fixed level of model performance falling 9 to 900 times a year, yet Menlo Ventures found enterprise spend on model APIs rose from $3.5 billion to $8.4 billion in about six months. Routing, prompt caching and spend caps bring it back under control, and Dilr Mira removes the per-token bill for regulated documents.
- Because the system around the model was never built: tests that score every change, limits on what the agent may do, and a record of what it did. Gartner forecasts that over 40% of agentic AI projects will be cancelled by the end of 2027. LangChain raised its coding agent from 52.8% to 66.5% on Terminal Bench 2.0 by changing only that surrounding system. Our AI Operating Model designs it.
- McKinsey Global Institute estimated about 1.8 hours a day, or 9.3 hours a week, spent searching for and gathering information. A knowledge base that answers from your own documents and cites the source cuts that time and makes a wrong answer traceable. Dilr Voice answers phone calls from exactly this kind of knowledge base.
- Start with the one you can already measure. A small business losing calls can be live the same day with an AI receptionist at $0.14 a minute plus a monthly fee per phone number, with $10 of trial credit. A larger organisation should start with a decision, not a product: the Placement Diagnostic takes four to six weeks and returns a ranked roadmap.
How do I get started?
Book a free consultation and tell us which problem costs you most
In 30 minutes we will tell you whether AI can fix it, what it would take and what it would be worth. If the honest answer is no, we say so.
