LLMs don't do math. They predict what math looks like.
We build a computation layer alongside your LLM stack. When a numerical output is needed - projections, estimates, scores, benchmarks - real code executes against real data. Verified results come back. The LLM handles reasoning and language. The computation layer handles truth.
We solve one specific problem: LLMs failing on structured data. Three service tiers: AI Accuracy Audits ($2K to $5K, 1 to 2 weeks), Custom AI Agent Builds ($10K to $30K, 4 to 8 weeks), and ongoing AI Monitoring retainers ($2K to $5K per month).
We work with: → VPs of Product carrying quiet dread about what's live in production → AI-first founders softening answers in enterprise due diligence → Engineering teams who've tried output guardrails and know it's not enough Industries: Fintech · HR Tech · Accounting & Tax · Analytics & BI · E-commerce · Insurance & Legal Tech Our work spans sales enablement, process automation, multi-agent AI systems, and internal tooling - built for businesses that need production- ready solutions, not proofs of concept. ━━━━━━━━━━━━━━━━━━━━━━
What we build: → AI-powered sales and estimation tools → Automated reporting and proposal generation → Multi-agent workflow systems → CRM and operations integrations ━━━━━━━━━━━━━━━━━━━━━━
Clients see results like: → 85% reduction in manual prep time → 25% shorter sales cycles → 40% increase in qualified leads → Deployment in under 3 weeks
━━━━━━━━━━━━━━━━━━━━━━
🌐 dojolabs.co 📧 [email protected] Islamabad, Pakistan · Wyoming, USA · Serving US, UK & Europe
Website
https://dojolabs.co
LLMs don't do math. They predict what math looks like.
We build a computation layer alongside your LLM stack. When a numerical output is needed - projections, estimates, scores, benchmarks - real code executes against real data. Verified results come back. The LLM handles reasoning and language. The computation layer handles truth.
We solve one specific problem: LLMs failing on structured data. Three service tiers: AI Accuracy Audits ($2K to $5K, 1 to 2 weeks), Custom AI Agent Builds ($10K to $30K, 4 to 8 weeks), and ongoing AI Monitoring retainers ($2K to $5K per month).
We work with: → VPs of Product carrying quiet dread about what's live in production → AI-first founders softening answers in enterprise due diligence → Engineering teams who've tried output guardrails and know it's not enough Industries: Fintech · HR Tech · Accounting & Tax · Analytics & BI · E-commerce · Insurance & Legal Tech Our work spans sales enablement, process automation, multi-agent AI systems, and internal tooling - built for businesses that need production- ready solutions, not proofs of concept. ━━━━━━━━━━━━━━━━━━━━━━
What we build: → AI-powered sales and estimation tools → Automated reporting and proposal generation → Multi-agent workflow systems → CRM and operations integrations ━━━━━━━━━━━━━━━━━━━━━━
Clients see results like: → 85% reduction in manual prep time → 25% shorter sales cycles → 40% increase in qualified leads → Deployment in under 3 weeks
━━━━━━━━━━━━━━━━━━━━━━
🌐 dojolabs.co 📧 [email protected] Islamabad, Pakistan · Wyoming, USA · Serving US, UK & Europe
Website
https://dojolabs.co
Location and contacts
Major clients
Processes and approach
How do you gather and validate client requirements?
We start with a discovery call to define the exact workflow where AI accuracy is failing. We pull sample inputs, edge cases, and expected outputs from the client's real data. Requirements are documented in a scope doc before work begins. For AI builds, every feature must pass defined accuracy thresholds on real client data before we consider it complete.
How do you ensure alignment with client goals and business strategy?
Every engagement starts with one defining metric: what does success look like in production? For accuracy work, that means a specific threshold documented in writing before work starts. We check against it at every milestone. If the goal changes mid-engagement, we update the scope doc and both parties confirm before continuing.
Which software development methodologies do you use (e.g., Agile, Waterfall, Scrum)?
Lightweight Agile with two-week sprints for longer builds. Milestone model for shorter audits: define deliverable, build it, verify it meets the accuracy threshold, deliver it. Primary tools: GitHub for version control, Python testing frameworks for validation gates. We keep process lightweight for small engineering teams.
How do you keep clients and stakeholders updated on project progress?
Clients get a written update at each sprint or milestone boundary. For short engagements (1 to 2 weeks): end-of-day check-ins. For longer builds: shared project tracker updated in real time. Every deliverable includes a written summary of what was built, what was tested, and the accuracy results. No surprises at handoff.
How frequently do you hold check-in meetings or status updates?
For a 1 to 2 week audit: one kickoff call and one delivery call. For a 4 to 8 week build: weekly check-in, plus async updates between calls. We keep meeting load low because our clients are technical founders who prefer written updates. If something needs a call, we call. If it can be a message, it is a message.
What quality assurance practices do you follow?
All AI outputs are validated against predefined accuracy thresholds before delivery. For every feature handling structured data, we build a test suite using real client inputs. LLM systems include schema validation, confidence thresholds, and deterministic fallback logic so the model escalates rather than guesses when uncertain. Production deployments include logging to catch accuracy drift early.
How do you identify and manage project risks?
Risks are identified at scoping: data gaps, edge case ambiguity, third-party API dependencies. Each risk gets a documented mitigation before work starts. Mid-engagement, risks are tracked with owner and status. Nothing is held silently.
What kind of support or maintenance do you offer after delivery?
All builds include a 30-day bug-fix window at no additional cost. After that, clients can move to our AI Monitoring retainer ($2K to $5K per month): continuous accuracy monitoring, monthly reports, model tuning, and direct engineering access. For audits, we deliver a written remediation plan the internal team can implement independently or pass back to us for the build phase.