People are doing machine work
Staff reading, classifying and re-keying thousands of documents, emails or tickets a month. Expensive, slow and inconsistent.
Service 05 / 14
AI Solutions
Most AI projects fail because they start with the technology instead of a problem worth solving. We start with a workflow that costs you real money, and only ship when the numbers hold up in evaluation.
- Typical duration
- 8–20 weeks
- Engagement
- Team of 2–4 · Discovery then build
- Right for you if
- Teams manually processing high volumes of documents or tickets
- Companies sitting on knowledge nobody can search
- Products where a natural-language interface removes real friction
- Businesses burned by an AI pilot that never reached production
Problems
You're probably here because of one of these.
If more than one sounds familiar, that's normal — they usually share a root cause.
Your knowledge isn't findable
Answers exist across wikis, tickets, PDFs and chat history, but nobody can retrieve them when it matters.
The demo worked, production didn't
A prototype that impressed in the meeting and fell apart on real data, with no evaluation harness to explain why.
You can't trust the output
No grounding, no citations, no confidence signal — so a human re-checks everything and the savings evaporate.
Approach
How the work actually runs.
From first conversation to running in production. Typical end-to-end duration for ai solutions is 8–20 weeks.
- 01
Opportunity assessment
1–2 weeksWe find the workflows where AI actually pays, and say plainly which ones don't.
- Workflow audit with time and cost per task measured
- Feasibility assessment against your real data
- Honest go / no-go recommendation per use case
- Projected return with the assumptions stated
- 02
Evaluation harness
1–2 weeksBefore building anything we define what correct means and how we'll measure it. This is the step most projects skip.
- Labelled evaluation dataset from your real cases
- Accuracy, latency and cost thresholds agreed up front
- Automated eval pipeline that runs on every change
- Baseline measurement of the current human process
- 03
Prototype
2–4 weeksThe smallest system that could work, measured against the harness rather than a demo.
- Retrieval pipeline over your own content
- Prompt and model selection driven by eval scores
- Cost-per-request modelled at your real volume
- Documented failure modes and their frequency
- 04
Production build
4–8 weeksHardened, observable and integrated into the tools your team already uses.
- Grounded responses with citations to source documents
- Human-in-the-loop review for low-confidence outputs
- Rate limiting, cost controls and prompt-injection defenses
- Integration into existing internal tooling
- 05
Launch
1–2 weeksRolled out to a subset first, with quality tracked against the human baseline.
- Staged rollout with per-cohort quality tracking
- Live dashboards for accuracy, latency and spend
- Escalation path when the system is uncertain
- Team training on capabilities and limits
- 06
Operate and improve
OngoingModels and data drift. We re-run evaluations continuously and keep the system honest.
- Continuous evaluation against a growing test set
- Model version upgrades validated before rollout
- Cost optimization as usage patterns emerge
- Quarterly review of accuracy and realised return
Included
What you get, spelled out.
Every item here is part of the engagement, not an upsell discovered halfway through.
Evaluation-first delivery
We measure quality before we build, so 'is it good enough' has a numeric answer.
Retrieval over your data
Answers grounded in your documents, with citations back to the source.
Human-in-the-loop review
Low-confidence outputs route to a person instead of being asserted confidently.
Cost controls
Per-request cost modelling, caching and rate limits so spend stays predictable at scale.
Prompt-injection defenses
Untrusted content is treated as data. Tool access is scoped and audited.
Model portability
An abstraction layer so you can move between providers as the frontier moves.
Observability
Every request traced with inputs, outputs, latency and cost for debugging and audit.
Honest scoping
If a use case won't clear the bar, we tell you during assessment rather than after the invoice.
Stack
What we typically build this with.
A default, not a rule. If your team already runs something else and it fits the requirement, we work in your stack.
Python
The default for data and model work. The ecosystem is unmatched and the talent pool is deep.
pgvector
Vector search inside Postgres. One database to operate instead of two, until scale genuinely demands otherwise.
TypeScript
Types across the whole stack. Most integration bugs stop being possible.
PostgreSQL
Our default. Transactional integrity, JSON, full-text search and row-level security in one engine.
Redis
Caching, rate limiting, queues and sessions. Boring in the best way.
Docker
The same image in dev, CI and production. 'Works on my machine' ends here.
AWS
Widest service coverage and the compliance posture enterprises expect.
Self-hosted
Your own servers, your own datacentre, or a VPS you control. Same containers, same pipeline, no cloud lock-in.
Why Devsock
The difference is in how the work is run.
Ten dimensions where engagements usually go wrong, and what we do instead. Every claim in the right-hand column is something you can verify in the first two weeks.
DimensionTypical agencyDevsock
Estimates
TypicalAn hourly rate and a range that grows once work starts.
DevsockFixed scope and fixed price after a paid discovery. Changes are quoted before they enter a sprint.
Communication
TypicalA weekly status email written by an account manager.
DevsockDirect access to the engineers building it, a shared board you can read any time, and a demo every second week.
Code ownership
TypicalDelivered at the end, sometimes with a proprietary framework attached.
DevsockYour repository, your cloud account, from commit one. No proprietary layer, no licensing.
Testing
TypicalManual clicking before launch, if the timeline allows.
DevsockAutomated suites in CI, load testing at projected peak, and a WCAG 2.2 AA audit before release.
Architecture
TypicalWhatever the available developer knows best.
DevsockA written architecture decision record explaining what we chose, what we rejected, and why.
Security
TypicalAddressed if the client raises it.
DevsockDependency scanning in CI, least-privilege access, secrets management and a pre-launch review as standard.
Accessibility
TypicalOut of scope unless legally forced.
DevsockWCAG 2.2 AA designed in and audited. Keyboard and screen-reader tested, not assumed.
Handover
TypicalA zip file and a phone number that stops answering.
DevsockDocumented architecture, runbooks, and a paid handover period so your team can genuinely take over.
After launch
TypicalA new quote for every bug.
DevsockDefect fixes covered for 90 days. Support retainers carry response-time SLAs in the contract.
Saying no
TypicalEvery request becomes a line item.
DevsockWe'll tell you when a feature isn't worth building, or when the answer isn't software at all.
Work
What we've built, and what it changed.
Live products you can open and use. Each one names the constraint, the approach and what shipped.
- Next.js
- TypeScript
- React Native
- PostgreSQL
- Redis
- Stripe
- AWS
Multi-tenant SaaS platform and driver apps for dumpster rental operators
Challenge
Dumpster rental operators run bookings, fleet position, driver assignments and invoicing across spreadsheets, whiteboards and phone calls. Nobody knows where a container actually is, proof of service arrives days after the job, and billing gets reconstructed from memory at month end.
Solution
A multi-tenant SaaS platform with self-serve signup, subscription billing and tiered plans, paired with native driver apps on iOS and Android. Operators get real-time container status, driver scheduling with route optimisation, customer and contract management, and automated invoicing. Drivers receive job assignments and upload proof of service from the field.
What shipped
- Native driver apps published to both the App Store and Google Play
- Multi-tenant architecture with per-operator data isolation
- Self-serve onboarding with a 15-day trial and three subscription tiers
- Real-time inventory and container status tracking
- Driver scheduling with route optimisation
- Automated invoicing and payment processing
- Next.js
- TypeScript
- Tailwind CSS
- Vercel
Corporate platform for an independent renewable-energy consultancy
Challenge
An independent monitoring and evaluation consultancy for renewable energy projects needed to establish credibility with government, industrial and hospital buyers — a sector where procurement turns on verifiable regulatory standing and a demonstrable track record, not on marketing copy.
Solution
A structured corporate platform built around proof rather than persuasion: service definitions for independent oversight and technical training, a credentials section surfacing regulatory compliance across SECP, FBR, PRA and PEC, team profiles establishing sector experience, and a documented track record of verified installations. Enquiries route directly to the consultancy team.
What shipped
- Structured service architecture across oversight and training lines
- Regulatory credentials surfaced as the primary trust signal
- Track record documented across government, industrial and public-health installations
- Team credibility profiles with sector experience
- Enquiry capture routed to the consultancy team
- Server-rendered and fully indexable
- Next.js
- TypeScript
- PostgreSQL
- Tailwind CSS
- AWS
Training platform with multi-country scheduling and certificate verification
Challenge
A QHSE training provider delivering internationally accredited certifications across Saudi Arabia, the UAE and Pakistan needed to publish a constantly moving multi-city schedule, take registrations online, and let employers independently verify that a certificate is genuine — the last of which is what makes an accreditation worth anything.
Solution
A training platform combining a published course schedule across multiple countries with online registration, a separate corporate training enquiry path, and a public certificate verification tool employers can use to authenticate credentials. Accreditation bodies and trainer profiles are presented as structured, checkable content rather than a logo wall.
What shipped
- Public certificate verification for employers
- Multi-country training schedule with online registration
- Separate paths for open-enrolment and corporate training
- Course catalogue spanning ISO, OSHA, IOSH, NVQ and Lean Six Sigma
- Accreditation bodies and trainer credentials as structured content
- Server-rendered and fully indexable
Further work sits behind NDAs. We can walk you through the architecture and measured outcomes of those engagements under mutual NDA.
Request a walkthroughFAQ
AI Solutions — common questions.
Specific to this service. The general questions about pricing, ownership and process are answered on the home page.
Still unsure? hello@devsock.com
No. We use enterprise API tiers with training explicitly disabled, and we can deploy inside your own cloud account or VPC where policy requires it. Data residency is a discovery question, not an afterthought.
Related
Usually paired with.
Most engagements combine two or three of these. We'll tell you which ones you actually need.
Next step
Let's scope your ai solutions project.
Bring us the problem rather than a specification. Thirty minutes with an engineer will tell you whether this is the right service, what it would take, and roughly what it would cost.
30 minutes
No slide deck, no sales team
An engineer
You speak to someone who builds
A straight answer
Including when it's don't build it